DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system.

This research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images. The model's performance is benchmarked against conventional CNNs and other recurrent architectures. The research addresses interpretability concerns by employing attention mechanisms that highlight salient features contributing to the model's decisions. The proposed model attention-gated recurrent units (A-GRU) results show promising results, indicating that the proposed model surpasses the state-of-the-art models in terms of accuracy and obtained 99.32% accuracy. Due to the high predictive capability of the proposed model, we recommend it for the effective diagnosis of Brain tumors in the E-healthcare system.

Relationship Between Cardiometabolic Index in Early Pregnancy and Hypertensive Disorder Complicating Pregnancy.

Background This study aimed to examine the cardiometabolic index during early pregnancy in individuals with hypertension-complicating pregnancy, especially preeclampsia. Additionally, this study sought to determine the relationship between cardiometabolic index and the incidence of varying degrees of preeclampsia. Methodology This study included 289 pregnant women diagnosed with preeclampsia who were registered and delivered at our hospital. These women were assigned to the preeclampsia group. Additionally, a group of 289 healthy pregnant women of identical gestational ages within the same time frame was included for comparison. Clinical data on pregnancy, including body mass index (BMI), blood pressure, waistline, triglyceride levels, and cardiometabolic index, were compared between the two groups. An analysis was conducted to examine the association between early pregnancy cardiometabolic index and the occurrence of preeclampsia. Results There was a significant association between the quartile of cardiometabolic index and the proportion of preeclampsia patients (p < 0.001). Furthermore, after controlling for age and BMI, the risk of preeclampsia remained significantly elevated and was associated with the cardiometabolic index. Conclusions A positive correlation was observed between cardiometabolic index during early pregnancy and the occurrence of preeclampsia.

Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.

Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation.

Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big challenge due to the complexity of brain tumor tissues, and segmenting these tissues from the healthy tissues is an even more tedious challenge when manual segmentation is undertaken by radiologists. In this paper, we have presented an experimental approach to emphasize the impact and effectiveness of deep learning elements like optimizers and loss functions towards a deep learning optimal solution for brain tumor segmentation. We evaluated our performance results on the most popular brain tumor datasets (MICCAI BraTS 2020 and RSNA-ASNR-MICCAI BraTS 2021). Furthermore, a new Bridged U-Net-ASPP-EVO was introduced that exploits Atrous Spatial Pyramid Pooling to enhance capturing multi-scale information to help in segmenting different tumor sizes, Evolving Normalization layers, squeeze and excitation residual blocks, and the max-average pooling for down sampling. Two variants of this architecture were constructed (Bridged U-Net_ASPP_EVO v1 and Bridged U-Net_ASPP_EVO v2). The best results were achieved using these two models when compared with other state-of-the-art models; we have achieved average segmentation dice scores of 0.84, 0.85, and 0.91 from variant1, and 0.83, 0.86, and 0.92 from v2 for the Enhanced Tumor (ET), Tumor Core (TC), and Whole Tumor (WT) tumor sub-regions, respectively, in the BraTS 2021validation dataset.

Hybrid Majority Voting: Prediction and Classification Model for Obesity.

Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack of exercise. Weight loss can be difficult for various reasons, and it is diagnosed via BMI, which is used to estimate body fat for most people. Muscular athletes, for example, may have a BMI in the obesity range even when they are not obese. Researchers from a variety of backgrounds and institutions devised different hypotheses and models for the prediction and classification of obesity using different approaches and various machine learning techniques. In this study, a majority voting-based hybrid modeling approach using a gradient boosting classifier, extreme gradient boosting, and a multilayer perceptron was developed. Seven distinct machine learning algorithms were used on open datasets from the UCI machine learning repository, and their respective accuracy levels were compared before the combined approaches were chosen. The proposed majority voting-based hybrid model for prediction and classification of obesity that was achieved has an accuracy of 97.16%, which is greater than both the individual models and the other hybrid models that have been developed.

Clinical assessment of a low-cost, hand-held, smartphone-attached intraoral imaging probe for 5-aminolevulinic acid photodynamic therapy monitoring and guidance.

Significance: India has one of the highest rates of oral squamous cell carcinoma (OSCC) in the world, with an incidence of 15 per 100,000 and more than 70,000 deaths per year. The problem is exacerbated by a lack of medical infrastructure and routine screening, especially in rural areas. New technologies for oral cancer detection and timely treatment at the point of care are urgently needed. Aim: Our study aimed to use a hand-held smartphone-coupled intraoral imaging device, previously investigated for autofluorescence (auto-FL) diagnostics adapted here for treatment guidance and monitoring photodynamic therapy (PDT) using 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence (FL). Approach: A total of 12 patients with 14 buccal mucosal lesions having moderately/well-differentiated micro-invasive OSCC lesions (<2 cm diameter and <5 mm depth) were systemically (in oral solution) administered three doses of 20 mg/kg ALA (total 60 mg/kg). Lesion site PpIX and auto-FL were imaged using the multichannel FL and polarized white-light oral cancer imaging probe before/after ALA administration and after light delivery (fractionated, total 100 J/cm2 of 635 nm red LED light). Results: The handheld device was conducive for access to lesion site images in the oral cavity. Segmentation of ratiometric images in which PpIX FL is mapped relative to auto-FL enabled improved demarcation of lesion boundaries relative to PpIX alone. A relative FL (R-value) threshold of 1.4 was found to segment lesion site PpIX production among the patients with mild to severe dysplasia malignancy. The segmented lesion size is well correlated with ultrasound findings. Lesions for which R-value was >1.65 at the time of treatment were associated with successful outcomes. Conclusion: These results indicate the utility of a low-cost, handheld intraoral imaging probe for image-guided PDT and treatment monitoring while also laying the groundwork for an integrated approach, combining cancer screening and treatment with the same hardware.

Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture.

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

U-Net-Based Models towards Optimal MR Brain Image Segmentation.

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture's performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.

Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things.

Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be automated with the potential of Artificial Intelligence (AI). However, challenges arise when dealing with sensitive data due to the dependence on large datasets. To address this issue, we propose an approach that combines different magnification factors of histopathological images using a residual network and information fusion in Federated Learning (FL). FL is employed to preserve the privacy of patient data, while enabling the creation of a global model. Using the BreakHis dataset, we compare the performance of FL with centralized learning (CL). We also performed visualizations for explainable AI. The final models obtained become available for deployment on internal IoMT systems in healthcare institutions for timely diagnosis and treatment. Our results demonstrate that the proposed approach outperforms existing works in the literature on multiple metrics.

DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms.

The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world's top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one's life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever.

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment.

The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the accurate detection of brain cancer. However, due to the inaccuracy of artificial diagnostic systems, medical professionals are not effectively incorporating them into the diagnosis process of Brain Cancer. In this research study, we proposed a robust brain tumor classification method using Deep Learning (DL) techniques to address the lack of accuracy issue in existing artificial diagnosis systems. In the design of the proposed approach, an improved convolution neural network (CNN) is used to classify brain tumors employing brain magnetic resonance (MR) image data. The model classification performance has improved by incorporating data augmentation and transfer learning methods. The results confirmed that the model obtained high accuracy compared to the baseline models. Based on high predictive results we suggest the proposed model for brain cancer diagnosis in IoT-healthcare systems.

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task.

An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model's ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.

IIMFCBM: Intelligent Integrated Model for Feature Extraction and Classification of Brain Tumors Using MRI Clinical Imaging Data in IoT-Healthcare.

Accurate classification of brain tumors is vital for detecting brain cancer in the Medical Internet of Things. Detecting brain cancer at its early stages is a tremendous medical problem, and many researchers have proposed various diagnostic systems; however, these systems still do not effectively detect brain cancer. To address this issue, we proposed an automatic diagnosing framework that will assist medical experts in diagnosing brain cancer and ensuring proper treatment. In developing the proposed integrated framework, we first integrated a Convolutional Neural Networks model to extract deep features from Magnetic resonance imaging. The extracted features are forwarded to a Long Short Term Memory model, which performs the final classification. Augmentation techniques were applied to increase the data size, thereby boosting the performance of our model. We used the hold-out Cross-validation technique for training and validating our method. In addition, we used various metrics to evaluate the proposed model. The results obtained from the experiments show that our model achieved higher performance than previous models. The proposed model is strongly recommended to be used to diagnose brain cancer in Medical Internet of Things healthcare systems due to its higher predictive outcomes.

Clinical evaluation of a mobile, low-cost system for fluorescence guided photodynamic therapy of early oral cancer in India.

BACKGROUND: Morbidity and mortality due to oral cancer in India are exacerbated by a lack of access to effective treatments amongst medically underserved populations. We developed a user-friendly low-cost, portable fibre-coupled LED system for photodynamic therapy (PDT) of early oral lesions, using a smartphone fluorescence imaging device for treatment guidance, and 3D printed fibreoptic attachments for ergonomic intraoral light delivery. METHODS: 30 patients with T1N0M0 buccal mucosal cancer were recruited from the JN Medical College clinics, Aligarh, and rural screening camps. Tumour limits were defined by external ultrasound (US), white light photos and increased tumour fluorescence after oral administration of the photosensitising agent ALA (60 mg/kg, divided doses), monitored by a smartphone fluorescence imaging device. 100 J/cm2 LED light (635 nm peak) was delivered followed by repeat fluorescence to assess photobleaching. US and biopsy were repeated after 7-17 days. This trial is registered with ClinicalTrials.gov, NCT03638622, and the study has been completed. FINDINGS: There were no significant complications or discomfort. No sedation was required. No residual disease was detected in 22 out of 30 patients who completed the study (26 of 34 lesions, 76% complete tumour response, 50 weeks median follow-up) with up to 7.2 mm depth of necrosis. Treatment failures were attributed to large tumour size and/or inadequate light delivery (documented by limited photobleaching). Moderately differentiated lesions were more responsive than well-differentiated cancers. INTERPRETATION: This simple and low-cost adaptation of fluorescenceguided PDT is effective for treatment of early-stage malignant oral lesions and may have implications in global health.

Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments.

Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping.

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms.

With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm's execution time.

Multichannel CNN Model for Biomedical Entity Reorganization.

Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.

DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems.

Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data. We have incorporated transfer learning (TL) and data augmentation (DA) mechanisms to improve the CNN model's predictive outcomes. For the fine-tuning process, the CNN model was trained with breast histology image data. Furthermore, the held-out cross-validation method for best model selection and hyper-parameter tuning was incorporated. In addition, various performance evaluation metrics for model performance assessment were computed. The experimental results confirmed that the proposed model outperformed the baseline models across all evaluation metrics, achieving 99.04% accuracy. We recommend the proposed method for early recognition of BC in IoMT healthcare systems due to its high performance.

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare.

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

Quantitative Insights Into ß-Lactamase Inhibitor's Contribution in the Treatment of Carbapenemase-Producing Organisms With ß-Lactams.

Objectives: Carbapenemase-producing organisms (CPOs) are associated with high mortality rates. The recent development of ß-lactamase inhibitors (BLIs) has made it possible to control CPO infections safely and effectively with ß-lactams (BLs). This study aims to explicate the quantitative relationship between BLI's ß-lactamase inhibition and CPO's BL susceptibility restoration, thereby providing the infectious disease society practical scientific grounds for regulating the use of BL/BLI in CPO infection treatment. Methods: A diverse collection of human CPO infection isolates was challenged by three structurally representative BLIs available in the clinic. The resultant ß-lactamase inhibition, BL susceptibility restoration, and their correlation were followed quantitatively for each isolate by coupling FIBA (fluorescence identification of ß-lactamase activity) and BL antibiotic susceptibility testing. Results: The ß-lactamase inhibition and BL susceptibility restoration are positively correlated among CPOs under the treatment of BLIs. Both of them are dependent on the target CPO's carbapenemase molecular identity. Of note, without sufficient ß-lactamase inhibition, CPO's BL susceptibility restoration is universally low across all tested carbapenemase molecular groups. However, a high degree of ß-lactamase inhibition would not necessarily lead to a substantial BL susceptibility restoration in CPO probably due to the existence of non-ß-lactamase BL resistance mechanisms. Conclusion: BL/BLI choice and dosing should be guided by quantitative tools that can evaluate the inhibition across the entire ß-lactamase background of the CPO upon the BLI administion. Furthermore, rapid molecular diagnostics for BL/BLI resistances, especially those sensitive to ß-lactamase independent BL resistance mechanisms, should be exploited to prevent ineffective BL/BLI treatment.