Unravelling the signatures of submarine groundwater discharge and seawater intrusion along the coastal plains of Odisha, India: a multi-proxy approach.

Submarine Groundwater Discharge (SGD) and Seawater Intrusion (SWI) are two contrary hydrological processes that occur across the land-sea continuum and understanding their nature is essential for management and development of coastal groundwater resource. Present study has attempted to demarcate probable zones of SGD and SWI along highly populated Odisha coastal plains which is water stressed due to indiscriminate-exploitation of groundwater leading to salinization and fresh groundwater loss from the alluvial aquifers. A multi-proxy investigation approach including decadal groundwater level dynamics, LANDSAT derived sea surface temperature (SST) anomalies and in-situ physicochemical analysis (pH, EC, TDS, salinity and temperature) of porewater, groundwater and seawater were used to locate the SGD and SWI sites. A total of 340 samples for four seasons (85 samples i.e., 30 porewater, 30 seawater and 25 groundwater in each season) were collected and their in-situ parameters were measured at every 1-2 km gap along ~ 145 km coastline of central Odisha (excluding the estuarine region). Considering high groundwater EC values (> 3000 µS/cm), three probable SWI and low porewater salinities (< 32 ppt in pre- and < 25 ppt in post-monsoons), four probable SGD zones were identified. The identified zones were validated with observed high positive hydraulic gradient (> 10 m) at SGD and negative hydraulic gradient (< 0 m) at SWI sites along with anomalous SST (colder in pre- and warmer in post-monsoon) near probable SGD locations. This study is first of its kind along the Odisha coast and may act as initial basis for subsequent investigations on fresh-saline interaction along the coastal plains where environmental integrity supports the livelihood of coastal communities and the ecosystem.

Correlation Between Radiological and Functional Outcomes Following Operative and Nonoperative Management of Acetabular Fractures: A Prospective Observational Study.

INTRODUCTION: The management of acetabular fractures is a complicated orthopedic procedure that has been advancing with time. Newer radiological tools like CT scans help surgeons to identify and manage these fractures more attentively. The study was conducted to evaluate the clinical and radiographic outcomes in patients with acetabular fractures managed either conservatively or by open reduction and internal fixation. MATERIALS AND METHOD: The study was done on 35 patients aged 18-60 years, with acetabular fractures treated either surgically or conservatively. Clinical scorings and radiological scoring were only taken and noted at three- and six-month intervals using Matta's radiographic scoring and modified Merle d'Aubigne and Postel clinical hip scoring. Clinico-radiological variables and complications were compared between the two groups. The data obtained was subjected to statistical analyses using IBM Statistical Package of Social Sciences (SPSS) 2.0 version software (Chicago, IL, USA) at a level of significance being p<0.05. RESULTS:  Out of a total of 35 patients, 19 were treated surgically and 16 conservatively. In patients belonging to the surgical treatment group, a maximum of 57.9% were aged 40-50 years, whereas the maximum patients (50%) of the conservative treatment group were aged <40 years, with male predominance in both groups. The type of fracture was recorded according to Judet and Letournel in both groups. Merle d'Aubigne's scoring and Matta's hip score were recorded at three and six months in both groups. A positive correlation was seen between radiological and functional outcomes at three and six months, which means that the higher the radiological scoring, the better the functional outcome of the patient managed either conservatively or surgically in the entire cohort. CONCLUSION:  Our study revealed that surgically managed patients had better functional and radiological outcomes than the patients who were conservatively managed at six months of follow-up. However, this is associated with more complications depending on fracture complexity and initial presentation of hip dislocation. The higher the radiological scoring, the better the functional outcome of the patient managed either conservatively or surgically in the entire cohort.

BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging.

Deep learning is a very important technique in clinical diagnosis and therapy in the present world. Convolutional Neural Network (CNN) is a recent development in deep learning that is used in computer vision. Our medical investigation focuses on the identification of brain tumour. To improve the brain tumour classification performance a Balanced binary Tree CNN (BT-CNN) which is framed in a binary tree-like structure is proposed. It has a two distinct modules-the convolution and the depthwise separable convolution group. The usage of convolution group achieves lower time and higher memory, while the opposite is true for the depthwise separable convolution group. This balanced binarty tree inspired CNN balances both the groups to achieve maximum performance in terms of time and space. The proposed model along with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. are experimented on public datasets. Before we feed the data into model the images are pre-processed using CLAHE, denoising, cropping, and scaling. The pre-processed dataset is partitioned into training and testing datasets as per 5 fold cross validation. The proposed model is trained and compared its perforarmance with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. The proposed model reported average training accuracy of 99.61% compared to other models. The proposed model achieved 96.06% test accuracy where as other models achieved 68.86%, 85.8%, 86.88%, and 90.41% respectively. Further, the proposed model obtained lowest standard deviation on training and test accuracies across all folds, making it invariable to dataset.

Stroke incidence, mortality, subtypes in rural and urban populations in five geographic areas of India (2018-2019): results from the National Stroke Registry Programme.

Background: Increasing stroke burden in India demands a long-term stroke surveillance framework. Earlier studies in India were urban-based, short term and provided limited data on stroke incidence and its outcomes. This gap is addressed by the establishment of five population-based stroke registries (PBSRs) of the National Stroke Registry Programme, India. This paper describes stroke incidence, mortality and age, sex, and subtypes distribution in the five PBSRs with urban and rural populations. Methods: First-ever incident stroke patients in age group ≥18 years, resident for at least one year in the defined geographic area, identified from health facilities were registered. Death records with stroke as the cause of death from the Civil Registration System (CRS) were included. Transient ischemic attack (TIA) was excluded. Three PBSRs (Cuttack, Tirunelveli, Cachar) included urban and rural populations. PBSRs in Kota and Varanasi were urban areas. The crude and age-standardized incidence rate (ASR) by age, sex, and residence (urban and rural), rate ratios of ASR, case fatality proportions and rates at day 28 after onset of stroke were calculated for years 2018-2019. Findings: A total of 13,820 registered first-ever stroke cases that included 985 death certificate-only cases (DCOs) were analysed. The pooled crude incidence rate was 138.1 per 100,000 population with an age-standardized incidence rate (ASR) of 103.4 (both sexes), 125.7 (males) and 80.8 (females). The risk of stroke among rural residents was one in seven (Cuttack), one in nine (Tirunelveli), and one in 15 (Cachar). Ischemic stroke was the most common type in all PBSRs. Age-standardized case fatality rates (ASCFR) per 100,000 population for pooled PBSRs was 30.0 (males) and 18.8 (females), and the rate ratio (M/F) ranged from 1.2 (Cuttack) to 2.0 (Cachar). Interpretation: Population-based registries have provided a comprehensive stroke surveillance platform to measure stroke burden and outcomes by age, sex, residence and subtype across India. The rural-urban pattern of stroke incidence and mortality shall guide health policy and programme planning to strengthen stroke prevention and treatment measures in India. Funding: The National Stroke Registry Programme is funded through the intramural funding of the Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, India.

Detection of COVID-19 using edge devices by a light-weight convolutional neural network from chest X-ray images.

Deep learning is a highly significant technology in clinical treatment and diagnostics nowadays. Convolutional Neural Network (CNN) is a new idea in deep learning that is being used in the area of computer vision. The COVID-19 detection is the subject of our medical study. Researchers attempted to increase the detection accuracy but at the cost of high model complexity. In this paper, we desire to achieve better accuracy with little training space and time so that this model easily deployed in edge devices. In this paper, a new CNN design is proposed that has three stages: pre-processing, which removes the black padding on the side initially; convolution, which employs filter banks; and feature extraction, which makes use of deep convolutional layers with skip connections. In order to train the model, chest X-ray images are partitioned into three sets: learning(0.7), validation(0.1), and testing(0.2). The models are then evaluated using the test and training data. The LMNet, CoroNet, CVDNet, and Deep GRU-CNN models are the other four models used in the same experiment. The propose model achieved 99.47% & 98.91% accuracy on training and testing respectively. Additionally, it achieved 97.54%, 98.19%, 99.49%, and 97.86% scores for precision, recall, specificity, and f1-score respectively. The proposed model obtained nearly equivalent accuracy and other similar metrics when compared with other models but greatly reduced the model complexity. Moreover, it is found that proposed model is less prone to over fitting as compared to other models.

Examining the rural-urban differentials in yoga and mindfulness practices among middle-aged and older adults in India: secondary analysis of a national representative survey.

Physical activity and mental well-being play an important role in reducing the risk of various diseases and in promoting independence among older adults. Appropriate physical activity, including yoga and mindfulness practices, can help rectify the loss of independence due to aging and have a positive influence on physical health and functional activities. This study assessed rural-urban differences in yoga and mindfulness practices and their associated factors among middle-aged and older Indian adults. The total sample size considered for the current analysis was 72,250 middle-aged and older adults (aged ≥ 45 years). Bivariate and multivariable logistic regression analyses were used to estimate the prevalence of yoga and mindfulness practices and examine the associations of selected variables with yoga and mindfulness practices among the participants. Further, we used the Fairley decomposition technique to determine the factors contributing to rural-urban differences in the prevalence of yoga and mindfulness practices among middle-aged and older adults. More than 9% of middle-aged and older adults in rural areas and 14% in urban areas reported practicing yoga and mindfulness activities more than once per week. Adults aged ≥ 65 years were more likely to practice yoga and mindfulness activities than those who age 45-54 years were. Those with an education of ten years and above were 2.3 and 2.1 times higher likely to practice yoga in rural (AOR: 2.28; CI: 2.07-2.52) and urban (AOR: 2.13; CI: 1.91-2.37) areas compared to their uneducated peers, respectively. The largest contributors in diminishing the gap in yoga practice among participants were education (44.2%), caste (2.5%), chronic diseases such as hypertension (4.53%), diabetes (1.71%), high cholesterol (3.08%), self-reported pain (5.76%), and difficulties in instrumental activities of daily living (1.22%). The findings suggest that middle-aged and older adults in urban areas practice yoga and mindfulness activities more than their peers in rural areas do. Education level, household characteristics, and health outcomes such as chronic conditions, pain, and functional difficulties explain the observed differences in yoga and mindfulness practices across rural and urban areas. Age-appropriate healthy practices such as yoga and mindfulness should be encouraged to enhance the physical and mental well-being of middle-aged and older adults, especially in rural areas.

The Pattern of Health-Seeking Behavior among Juang Tribe- A Particularly Vulnerable Tribal Group of Odisha, India.

Introduction: Social and economic position, environment, access to healthcare, ethos, and worldview are all important determinants of people's health-seeking behavior. Tribal communities in India oscillate between their emic rationale for the cause and treatment of health concerns on the one hand and affordable modern methods on the other. The present article is based on a study conducted among the Junag tribe of Odisha to understand their acceptance and preference for traditional or modern healthcare systems and the reasons for these choices. Material and Methods: Purposive sampling, a pre-structured schedule, an observation approach, case studies, and interviews with community members have been used to gather the primary data used in the study. Result: The study identified a diverse pattern of health-seeking behavior. From the four villages, 70% of respondents combined into cluster-1 preferred the traditional healthcare system, mostly for geographical and cultural reasons, as opposed to 95% of respondents from the villages in cluster-2, who preferred modern healthcare services because they were more easily accessible and involved modern healthcare providers. Conclusion: In this research work, it has been found that the Juang tribe is in a transitional phase and uses a hybrid approach to health seeking. Moreover, it has also been discovered that important variables including cultural attitudes and the affordability of contemporary healthcare services have influenced people's decisions toward healthcare systems.

A new efficient referential genome compression technique for FastQ files.

Hospitals and medical laboratories create a tremendous amount of genome sequence data every day for use in research, surgery, and illness diagnosis. To make storage comprehensible, compression is therefore essential for the storage, monitoring, and distribution of all these data. A novel data compression technique is required to reduce the time as well as the cost of storage, transmission, and data processing. General-purpose compression techniques do not perform so well for these data due to their special features: a large number of repeats (tandem and palindrome), small alphabets, and highly similar, and specific file formats. In this study, we provide a method for compressing FastQ files that uses a reference genome as a backup without sacrificing data quality. FastQ files are initially split into three streams (identifier, sequence, and quality score), each of which receives its own compression technique. A novel quick and lightweight mapping mechanism is also presented to effectively compress the sequence stream. As shown by experiments, the suggested methods, both the compression ratio and the compression/decompression duration of NGS data compressed using RBFQC, are superior to those achieved by other state-of-the-art genome compression methods. In comparison to GZIP, RBFQC may achieve a compression ratio of 80-140% for fixed-length datasets and 80-125% for variable-length datasets. Compared to domain-specific FastQ file referential genome compression techniques, RBFQC has a compression and decompression speed (total) improvement of 10-25%.

DMF-Net: a deep multi-level semantic fusion network for high-resolution chest CT and X-ray image de-noising.

Medical images such as CT and X-ray have been widely used for the detection of several chest infections and lung diseases. However, these images are susceptible to different types of noise, and it is hard to remove these noises due to their complex distribution. The presence of such noise significantly deteriorates the quality of the images and significantly affects the diagnosis performance. Hence, the design of an effective de-noising technique is highly essential to remove the noise from chest CT and X-ray images prior to further processing. Deep learning methods, mainly, CNN have shown tremendous progress on de-noising tasks. However, existing CNN based models estimate the noise from the final layers, which may not carry adequate details of the image. To tackle this issue, in this paper a deep multi-level semantic fusion network is proposed, called DMF-Net for the removal of noise from chest CT and X-ray images. The DMF-Net mainly comprises of a dilated convolutional feature extraction block, a cascaded feature learning block (CFLB) and a noise fusion block (NFB) followed by a prominent feature extraction block. The CFLB cascades the features from different levels (convolutional layers) which are later fed to NFB to attain correct noise prediction. Finally, the Prominent Feature Extraction Block(PFEB) produces the clean image. To validate the proposed de-noising technique, a separate and a mixed dataset containing high-resolution CT and X-ray images with specific and blind noise are used. Experimental results indicate the effectiveness of the DMF-Net compared to other state-of-the-art methods in the context of peak signal-to-noise ratio (PSNR) and structural similarity measurement (SSIM) while drastically cutting down on the processing power needed.

Impacts of climate change and coastal salinization on the environmental risk of heavy metal contamination along the odisha coast, India.

Climate change-mediated rise in sea level and storm surges, along with indiscriminate exploitation of groundwater along populous coastal regions have led to seawater intrusion. Studies on groundwater salinization and heavy metal contamination trends are limited. Present study investigated the heavy metal contamination, associated risks and provided initial information on the impacts of groundwater salinization on heavy metals along the coastal plains of Odisha, India. Total 50 groundwater samples (25 each in post- and pre-monsoon) were collected and analysed. Concentrations of Fe (44%), Mn (44%), As (4%) and Al (4%) in post-monsoon and Fe (32%), Mn (32%), As (4%), B (8%) and Ni (16%) in pre-monsoon exceeded Bureau of Indian Standards (BIS) drinking water limits. High concentrations of heavy metals (Fe, Sr, Mn, B, Ba, Li, Ni and Co) and high EC (>3000 µS/cm) indicated that the groundwater-seawater mixing process has enhanced the leaching and ion exchange of metallic ions in central part of the study area. Multivariate statistical analysis suggested leaching process, seawater intrusion and agricultural practices as the main heavy metal sources in the groundwater. 4% of samples in post- and 16% in pre-monsoon represented high heavy metal pollution index (HPI). Pollution indices indicated the central and south-central regions are highly polluted due to saline water intrusion and high agricultural activities. Ecological risks in the groundwater systems found low (ERI <110) in both seasons. Children population found more susceptible to health risks than adults. Hazard index (HI > 1) has shown significant non-carcinogenic risks where Fe, Mn, As, B, Li and Co are the potential contributors. Incremental lifetime cancer risk (ILCR >1.0E-03) has suggested high carcinogenic risks, where As and Ni are the major contributors. The study concluded that groundwater salinization could increase the heavy metal content and associated risks. This would help policymakers to take appropriate measures for sustainable coastal groundwater management.

Automated Detection and Segmentation of Exudates for the Screening of Background Retinopathy.

Exudate, an asymptomatic yellow deposit on retina, is among the primary characteristics of background diabetic retinopathy. Background diabetic retinopathy is a retinopathy related to high blood sugar levels which slowly affects all the organs of the body. The early detection of exudates aids doctors in screening the patients suffering from background diabetic retinopathy. A computer-aided method proposed in the present work detects and then segments the exudates in the images of retina acquired using a digital fundus camera by (i) gradient method to trace the contour of exudates, (ii) marking the connected candidate pixels to remove false exudates pixels, and (iii) linking the edge pixels for the boundary extraction of exudates. The method is tested on 1307 retinal fundus images with varying characteristics. Six hundred and forty-nine images were acquired from hospital and the remaining 658 from open-source benchmark databases, namely, STARE, DRIVE MESSIDOR, DiaretDB1, and e-Ophtha. The exudates segmentation method proposed in this research work results in the retinal fundus image-based (i) accuracy of 98.04%, (ii) sensitivity of 95.345%, and (iii) specificity of 98.63%. The segmentation results for a number of exudates-based evaluations depict the average (i) accuracy of 95.68%, (ii) sensitivity of 93.44%, and (iii) specificity of 97.22%. The substantial combined performance at image and exudates-based evaluations proves the contribution of the proposed method in mass screening as well as treatment process of background diabetic retinopathy.

Prevalence and pattern of multimorbidity among chronic kidney disease patients: a community study in chronic kidney disease hotspot area of Eastern India.

Introduction: Chronic kidney disease (CKD) is mostly asymptomatic until reaching an advanced stage. Although conditions such as hypertension and diabetes can cause it, CKD can itself lead to secondary hypertension and cardiovascular disease (CVD). Understanding the types and prevalence of associated chronic conditions among CKD patient could help improve screening for early detection and case management. Methods: A cross sectional study of 252 CKD patients in Cuttack, Odisha (from the last 4 years CKD data base) was telephonically carried out using a validated Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC) tool with the help of an android Open Data Kit (ODK). Univariate descriptive analysis was done to determine the socio-demographic distribution of CKD patients. A Cramer's heat map was generated for showing Cramer's coefficient value of association of each diseases. Results: The mean age of participants was 54.11 (±11.5) years and 83.7% were male. Among the participants, 92.9% had chronic conditions (24.2% with one, 26.2% with two and 42.5% with three or more chronic conditions). Most prevalent chronic conditions were hypertension (48.4%), peptic ulcer disease (29.4%), osteoarthritis (27.8%) and diabetes (13.1%). Hypertension and osteoarthritis were found to be most commonly associated (Cramer's V coefficient = 0.3). Conclusion: Increased vulnerability to chronic conditions among CKD patients make them at higher risk for mortality and compromised quality of life. Regular screening of CKD patient for other chronic conditions (hypertension, diabetes, peptic ulcer disease, osteoarthritis and heart diseases) would help in detecting them early and undertake prompt management. The existing national program could be leveraged to achieve this.

Classification of Brain Tumours in MRI Images using a Convolutional Neural Network.

INTRODUCTION: Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used machine learning algorithm for visual learning and image identification is CNN. METHODS: In this article, the convolutional neural network (CNN) technique is used. Augmentation of data and processing of images is used to classify scan imagery of brain MRI as malignant or benign. The performance of the proposed CNN model is compared with pre-trained models: VGG-16, ResNet-50, and Inceptionv3 using the technique which is transfer learning. RESULTS: Even though the experiment was conducted on a relatively limited dataset, the experimental results reveal that the suggested scratched CNN model accuracy achieved is 94 percent, VGG-16 was extremely effective and had a very low complexity rate with an accuracy of 90 percent, whereas ResNet- 50 reached 86 percent and Inception v3 obtained 64 percent accuracy. CONCLUSION: When compared to previous pre-trained models, the suggested model consumes significantly less processing resources and achieves significantly higher accuracy outcomes and reduction in losses.

Enhanced framework for COVID-19 prediction with computed tomography scan images using dense convolutional neural network and novel loss function.

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19.

GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force.

Walking (gait) irregularities and abnormalities are predictors and symptoms of disorder and disability. In the past, elaborate video (camera-based) systems, pressure mats, or a mix of the two has been used in clinical settings to monitor and evaluate gait. This article presents an artificial intelligence-based comprehensive investigation of ground reaction force (GRF) pattern to classify the healthy control and gait disorders using the large-scale ground reaction force. The used dataset comprised GRF measurements from different patients. The article includes machine learning- and deep learning-based models to classify healthy and gait disorder patients using ground reaction force. A deep learning-based architecture GaitRec-Net is proposed for this classification. The classification results were evaluated using various metrics, and each experiment was analysed using a fivefold cross-validation approach. Compared to machine learning classifiers, the proposed deep learning model is found better for feature extraction resulting in high accuracy of classification. As a result, the proposed framework presents a promising step in the direction of automatic categorization of abnormal gait pattern.

A novel service robot assignment approach for COVID-19 infected patients: a case of medical data driven decision making.

Coronavirus Disease-19 (COVID-19) is a major concern for the entire world in the current era. Coronavirus is a very dangerous infectious virus that spreads rapidly from person to person. It spreads in exponential manner on a global scale. It affects the doctors, nurse and other COVID-19 warriors those who are actively involved for the treatment of COVID-19 infected (CI) patients. So, it is very much essential to focus on automation and artificial intelligence (AI) in different hospitals for the treatment of such infected patients and all should be very much careful to break the chain of spreading this novel virus. In this paper, a novel patient service robots (PSRs) assignment framework and a priority based (PB) method using fuzzy rule based (FRB) approach is proposed for the assignment of PSRs for CI patients in hospitals in order to provide safety to the COVID-19 warriors as well as to the CI infected patients. This novel approach is mainly focused on lowering the active involvement of COVID-19 warriors for the treatment of high asymptotic COVID-19 infected (HACI) patients for handling this tough situation. In this work, we have focused on HACI and low asymptotic COVID-19 infected (LACI) patients. Higher priority is given to HACI patients as compared to LACI patients to handle this critical situation in order to increase the survival probability of these patients. The proposed method deals with situations that practically arise during the assignment of PSRs for the treatment of such patients. The simulation of the work is carried out using MATLAB R2015b.

HTLML: Hybrid AI Based Model for Detection of Alzheimer's Disease.

Alzheimer's disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brain's ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Naïve base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective.

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer.

Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers.

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm.

BACKGROUND: The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and wealth in a genuine way. METHODS: In this article, the authors endeavored to design a hospital management system with secured data processing. The proposed approach consists of three different phases. In the first phase, a smart healthcare system is proposed for providing an effective health service, especially to patients with a brain tumor. An application is developed that is compatible with Android and Microsoft-based operating systems. Through this application, a patient can enter the system either in person or from a remote place. As a result, the patient data are secured with the hospital and the patient only. It consists of patient registration, diagnosis, pathology, admission, and an insurance service module. Secondly, deep-learning-based tumor detection from brain MRI and EEG signals is proposed. Lastly, a modified SHA-256 encryption algorithm is proposed for secured medical insurance data processing which will help detect the fraud happening in healthcare insurance services. Standard SHA-256 is an algorithm which is secured for short data. In this case, the security issue is enhanced with a long data encryption scheme. The algorithm is modified for the generation of a long key and its combination. This can be applicable for insurance data, and medical data for secured financial and disease-related data. RESULTS: The deep-learning models provide highly accurate results that help in deciding whether the patient will be admitted or not. The details of the patient entered at the designed portal are encrypted in the form of a 256-bit hash value for secured data management.

Neonatal Joubert Syndrome With Renal Involvement and Respiratory Distress.

Joubert syndrome (JS) is a rare autosomal recessive neurodevelopmental disorder with characteristic clinical presentation of hyperpnea-apnea spells, hypotonia, dysmorphic facies, and nystagmus and imaging features of molar tooth sign and cerebellar vermian hypoplasia-dysplasia. Early diagnosis is needed for timely management and favorable outcome. We present a case of neonatal JS with renal involvement presenting with respiratory distress and highlight the characteristic clinical and imaging findings. On examination, the baby had low set ears, a large protruding tongue, hypertelorism, and a depressed nasal bridge. Ultrasonography (USG) abdomen showed echogenic kidneys with cortical and medullary cysts. Magnetic Resonance Imaging (MRI) brain showed classical molar tooth sign, vermian hypoplasia-dysplasia, and thinning of the corpus callosum.