Deep neural network for food image classification and nutrient identification: A systematic review.

Technology impacts human life in both the aspects such as positive and negative, which helps in better communication and eliminating geographical boundaries. However, social media and mobile devices may lead to severe health conditions such as sleep problems, depression, obesity, etc. A systematic review is conducted to analyze health issues by tracking food intake by considering positive aspects using Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Guidelines. The major scientific databases (such as Web of Science, Scopus, and IEEE explore) are explored to search the image recognition and analysis articles. The search query is applied to the databases using keywords like "Food Image," "Food Image Classification," "Nutrient Identification," "Nutrient Estimation," and using "Machine Learning," etc. 771 articles are extracted from these databases, and 56 are identified for final consideration after rigorous screening. A few investigations are extracted based on available food image datasets, hyperparameters tuning, a technique used, performance metrics, and challenges of Food Image Classification (FIC). This study discusses different investigations with their proposed FIC and nutrient estimation solution. Finally, this intensive research presents a case study using FIC and object detection techniques to estimate nutrition with food image analysis.

Pattern of nutritional status in node-negative versus node-positive head and neck cancer patients undergoing treatment: a prospective cohort study.

PURPOSE: The aim of this study was to study the nutritional profile of node-negative and node-positive patients undergoing treatment for head and neck squamous cell cancer (HNSCC). METHODS: This prospective cohort study was conducted between 2018 and 2020. Patients diagnosed with HNSCC, planned for treatment, were enrolled after written informed consent. In node-negative (N0) and node-positive (N +) cohorts of patients, nutritional status was determined using anthropometric measures and Subjective Global Assessment (SGA) scale pre-treatment, and during and after treatment. Statistical analysis was performed using SPSS version 22. Data was analyzed using parametric and non-parametric tests, and p value of 0.05 was considered significant. RESULTS: In total, 161 patients were analyzed, 73 N0 and 88 N + cohorts. Pre-treatment, 9.6 to 20.4% patients in N0 and 23.9 to 32.8% patients in N + cohorts were malnourished. Incidence of malnutrition at completion of treatment was 40.8 to 52.5% overall, 20.5 to 41.1% N0, and 39.5 to 62.8% N + . Mean reduction in weight (11.1% ± 7.82 vs 6.26% ± 8.3, p = 0.000), mean reduction in BMI (2.57 ± 1.87 vs 1.29 ± 1.62, p = 0.000), median reduction in MUAC (2 cm vs 1 cm, p = 0.000), and median increase in SGA score (13 vs 6, p = 0.000) were higher in multi-modality as compared to those in a single-modality treatment. Similar findings were noted in N0 and N + cohorts. CONCLUSION: As compared to N0, N + patients had higher burden of malnutrition at diagnosis, and more worsening of nutritional parameters during treatment. More decline in nutritional status was seen in patients receiving multi-modality as compared to single-modality treatment.

DTLMV2-A real-time deep transfer learning mask classifier for overcrowded spaces.

Through the commencement of the COVID-19 pandemic, the whole globe is in disarray and debating on unique approaches to stop this viral transmission. Masks are being worn by people all around the world as one of the preventative measures to avoid contracting this sickness. Although some people are following and adopting this precaution, others are not, despite official recommendations from the administration and public health organisations has been announced. In this paper DTLMV2 (Deep Transfer Learning MobileNetV2 for the objective of classification) is proposed - A face mask identification model that can reliably determine whether an individual is wearing a mask or not is suggested and implemented in this work. The model architecture employs the peruse of MobileNetV2, a lightweight Convolutional Neural Network (CNN) that requires less computing power and can be readily integrated into computer vision and mobile systems. The computer vision with MobileNet is required to formulate a low-cost mask detection system for a group of people in open spaces that can assist in determining whether a person is wearing a mask or not, as well as function as a surveillance system since it is effective on both real-time pictures and videos. The face recognition model obtained 97.01% accuracy on validation data, 98% accuracy on training data and 97.45% accuracy on testing data.

Study of dosimetry and clinical factors for assessment of xerostomia in head and neck squamous cell carcinoma treated by intensity-modulated radiotherapy: A prospective study.

AIM: Clinical and dosimetric factors related to toxicity in terms of xerostomia in patients with head and neck squamous cell cancer (HNSCC) treated with intensity-modulated radiotherapy (IMRT). MATERIALS AND METHODS: Patients older than 18 years, with the WHO Performance Status Score <2 with primary diagnosis of HNSCC Stage II, III, and IV who had undergone primary or postoperative radiotherapy (RT) treated by IMRT at the center, from November 2015 to November 2016 were included in the study. Patients were assessed by physical examination and questioned to score their quality of life for dryness (HNDR) and stickiness (HNSS) by EORTC-HN-35 (Hindi or English version) at baseline (before treatment), at 3, 6, and 12 months following treatment. The validation of EORTC-HN-35 for HNDR and HNSS in patients was handed. RESULTS: Thirty patients were included in the study. The mean symptom score values for HNSS at baseline, 3, 6, and 12 months' post-RT treatment were 17.8, 62.2, 64.4, and 20.8, respectively. Dryness and stickiness also increased over 3-6 months in follow-up but slightly relieved at 12 months, but it could not reach to baseline. In subgroup analysis, at baseline mean score of dryness of mouth in elderly patients (≥60 years) (P = 0.248), poor performance status (Eastern Cooperative Oncology Group 2) (P = 0.80) and patients with advanced stage (Stage III and IVA) (P = 0.185) was higher. Correlation of normal tissue complication probability for xerostomia with contralateral mean parotid gland showed insignificant linearity with shallow curve. CONCLUSION: Patients remained symptomatic for xerostomia chiefly till 6 months' postirradiation, but it was slightly relieved in 12 months but could not reach the baseline. Dosimetric sparing ofcontralateral parotid resulted in decreased probability of developing xerostomia.

Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections.

Virus diseases are a continued threat to human health in both community and healthcare settings. The current virus disease COVID-19 outbreak raises an unparalleled public health issue for the world at large. Wuhan is the city in China from where this virus came first and, after some time the whole world was affected by this severe disease. It is a challenge for every country's people and higher authorities to fight with this battle due to the insufficient number of resources. On-going assessment of the epidemiological features and future impacts of the COVID-19 disease is required to stay up-to-date of any changes to its spread dynamics and foresee needed resources and consequences in different aspects as social or economic ones. This paper proposes a prediction model of confirmed and death cases of COVID-19. The model is based on a deep learning algorithm with two long short-term memory (LSTM) layers. We consider the available infection cases of COVID-19 in India from January 22, 2020, till October 9, 2020, and parameterize the model. The proposed model is an inference to obtain predicted coronavirus cases and deaths for the next 30 days, taking the data of the previous 260 days of duration of the pandemic. The proposed deep learning model has been compared with other popular prediction methods (Support Vector Machine, Decision Tree and Random Forest) showing a lower normalized RMSE. This work also compares COVID-19 with other previous diseases (SARS, MERS, h1n1, Ebola, and 2019-nCoV). Based on the mortality rate and virus spread, this study concludes that the novel coronavirus (COVID-19) is more dangerous than other diseases.

Deep learning based detection and analysis of COVID-19 on chest X-ray images.

Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.

Cancer Pain, Anxiety, and Depression in Admitted Patients at a Tertiary Care Hospital: A Prospective Observational Study.

CONTEXT: Pain is the most common symptom in admitted cancer patients. The association between the severity of cancer pain and distress symptoms such as depression and anxiety is a subject of research. AIMS: The aim is to study the prevalence of pain, anxiety, and depression in admitted cancer patients and determine the association between pain and anxiety and depression at a tertiary cancer care institute. SETTINGS AND DESIGN: This was prospective observational study. SUBJECTS AND METHODS: We enrolled 393 cancer inpatients prospectively after written informed consent. Their disease details, presence, severity, and character of pain were recorded. Numerical Pain Scale was used for pain scores, self-reporting Hospital Anxiety and Depression Scale for anxiety and depression. STATISTICAL ANALYSIS USED: Normal data were analyzed with parametric, nonnormal with nonparametric methods, and categorical with the Chi-square test. RESULTS: The prevalence of moderate-to-severe pain was 41.5%, anxiety 20.3%, and depression 24.8%. Proportion of patients with anxiety and depression was 9.2% and 17.7% in patients with no pain; about 32.8% and 36.7% with severe pain, respectively (P < 0.000). In patients with no depression 6% had anxiety; with depression 44.9% had anxiety (P < 0.000). Odd's ratio to have anxiety and depression was 4.44 (95% confidence interval [CI] 2.0318-9.7024) and 2.92 (95% CI 1.5739-5.4186), respectively, in patients with pain as compared to no pain (P < 0.00). There was a positive correlation between pain, anxiety, and depression scores. CONCLUSIONS: There is strong association between the presence and severity of pain and distress symptoms such as anxiety and depression in admitted cancer patients.

Os-GIGANTEA confers robust diurnal rhythms on the global transcriptome of rice in the field.

The circadian clock controls physiological traits such as flowering time, photosynthesis, and growth in plants under laboratory conditions. Under natural field conditions, however, little is known about the significance of the circadian clock in plants. By time-course transcriptome analyses of rice (Oryza sativa) leaves, using a newly isolated rice circadian clock-related mutant carrying a null mutation in Os-GIGANTEA (Os-GI), we show here that Os-GI controlled 75% (false discovery rate = 0.05) of genes among 27,201 genes tested and was required for strong amplitudes and fine-tuning of the diurnal rhythm phases of global gene expression in the field. However, transcripts involved in primary metabolism were not greatly affected by osgi. Time-course metabolome analyses of leaves revealed no trends of change in primary metabolites in osgi plants, and net photosynthetic rates and grain yields were not affected. By contrast, some transcripts and metabolites in the phenylpropanoid metabolite pathway were consistently affected. Thus, net primary assimilation of rice was still robust in the face of such osgi mutation-related circadian clock defects in the field, unlike the case with defects caused by Arabidopsis thaliana toc1 and ztl mutations in the laboratory.

Evaluation of toxicological impact of cartap hydrochloride on some physiological activities of a non-heterocystous cyanobacterium Leptolyngbya foveolarum.

The present study was aimed to the evaluation of toxicological impact of insecticide cartap hydrochloride on photosynthesis and nitrogen assimilation of a non-heterocystous cyanoprokaryote Leptolyngbya foveolarum isolated from paddy fields of Punjab, India. The microorganism tolerated commercial grade insecticide up to 80 ppm. Lower concentration (20 ppm) of cartap supported good growth with high dry weight of biomass, total protein content, photosynthetic pigments, photosynthesis and respiration compared to untreated control cultures while higher concentrations (40 and 60 ppm) inhibited these parameters in a dose dependent manner. Treatment of the microorganism with 60 ppm cartap lowered the content of photosynthetic pigments with maximum inhibitory effect on phycoerythrin (70% decrease) followed by allophycocyanin (66% decrease). Rates of photosynthesis and respiration were inhibited by 63% and 45%, respectively, while PS-I, II and whole chain activity were decreased by 45%, 67% and 40% respectively, compared to untreated control cultures. Cartap at 60 ppm decreased nitrate and nitrite uptake by 31% and 61%, respectively, whereas uptake of ammonium was slightly increased (18%) in cartap (60 ppm) treated cells. Nitrate and nitrite reductase, and glutamine synthetase activities of the microorganism decreased by 36-50% in 60 ppm cartap. The low levels of growth, photosynthetic pigments and activities of nitrogen assimilating enzymes in cells grown in nitrogen depleted medium supplement with insecticide indicated that insecticide may be used by the organism as a nitrogen source.

Production and characterization of an organic solvent activated protease from haloalkaliphilic bacterium Halobiforma sp. strain BNMIITR.

An unusual haloalkaliphilic bacterium known as Halobiforma sp. strain BNMIITR, which was noticed to produce an extracellular alkaline protease, was found in a soil sample from Northern India's Sambhar Lake. On the generation of protease, the effects of dietary elements including nitrogen and carbon sources, amino acids, and growth conditions like temperature and pH were investigated. When low-cost agricultural by-products were employed as nitrogen sources, the manufacturing of enzymes was significantly boosted. In the present study, protease production was enhanced by 2.94 fold and 2.17 fold. By solvent precipitation and Hydrophobic interaction chromatography (HIC) on Phenyl Sepharose 6 Fast Flow matrix, the enzyme was purified 31.67 fold. It was determined that the apparent molecular mass was 21 kDa. The pH range where the enzyme was most stable was 6.0-12.0, with a temperature of 50 °C as optimum. When there was alkaline earth metals and heavy metals, protease was discovered to be active. It was evident that the enzyme was a serine type of protease because it was active in the presence of a variety of surfactants, oxidizing and reducing chemicals, and phenylmethylsulfonyl fluoride (PMSF) completely inhibited activity. Enzyme exhibited a wide range of substrate specificity. Amazingly, enzyme remained stable both in polar and nonpolar solvents. The most interesting aspect of this enzyme is enhanced activity in polar solvents like dimethylformamide (DMF) and dimethyl sulfoxide (DMSO). It was discovered that the protease was stable and compatible with a number of widely available detergents.

SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification.

BACKGROUND: Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification. OBJECTIVE: This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques. METHODS: This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets. RESULTS: SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets. CONCLUSION: These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.

A translationally controlled tumor protein negatively regulates the hypersensitive response in Nicotiana benthamiana.

We have been isolating and characterizing Ralstonia solanacearum-responsive genes (RsRGs) in Nicotiana plants. In this study we focused on RsRG308, which we renamed NbTCTP (N. benthamiana translationally controlled tumor protein) because it encodes a polypeptide showing similarity to translationally controlled tumor proteins. Induction of the hypersensitive response (HR) was accelerated in NbTCTP-silenced N. benthamiana plants challenged with R. solanacearum 8107 (Rs8107). The Rs8107 population decreased significantly, whereas hin1 gene expression was enhanced in the silenced plant. Accelerated induction of HR was observed in NbTCTP-silenced plants inoculated with Pseudomonas cichorii and P. syringae pv. syringae. Silencing of NbTCTP also accelerated the induction of HR cell death by Agrobacterium-mediated transient expression of HR inducers, such as AvrA, BAX, INF1 and NbMEK2(DD). NbTCTP silencing enhanced NbrbohB- and NbMEK2-mediated reactive oxygen species production, leading to HR. Transient expression of both the full-length sequence and the Bcl-xL domain of NbTCTP decreased HR cell death induced by Agrobacterium-mediated transient expression of HR inducers. NbTCTP-silenced plants also showed slightly dwarf phenotypes. Therefore, NbTCTP might have a role in cell death regulation during HR to fine-tune programmed cell death-associated plant defense responses.

Bioprocess development for nicotinic acid hydroxamate synthesis by acyltransferase activity of Bacillus smithii strain IITR6b2.

In this work, acyltransferase activity of a new bacterial isolate Bacillus smithii strain IITR6b2 was utilized for the synthesis of nicotinic acid hydroxamate (NAH), a heterocyclic class of hydroxamic acid. NAH is an important pyridine derivative and has found its role as bioligand, urease inhibitor, antityrosinase, antioxidant, antimetastatic, and vasodilating agents. Amidase having acyltransferase activity with nicotinamide is suitable for nicotinic acid hydroxamate production. However, amidase can also simultaneously hydrolyze nicotinamide and nicotinic acid hydroxamate to nicotinic acid. Nicotinic acid is an undesirable by-product and thus any biocatalytic process involving amidase for nicotinic acid hydroxamate production needs to have high ratios of acyltransferase to amide hydrolase and acyltransferase to nicotinic acid hydroxamate hydrolase activity. Isolate Bacillus smithii strain IITR6b2 was found to have 28- and 12.3-fold higher acyltransferase to amide and hydroxamic acid hydrolase activities, respectively. This higher ratio resulted in a limited undesirable by-product, nicotinic acid (NA) synthesis. The optimal substrate/co-substrate ratio, pH, temperature, incubation time, and resting cells concentration were 200/250 mM, 7, 30 °C, 40 min, and 0.7 mg(DCW) ml(-1), respectively, and 94.5 % molar conversion of nicotinamide to nicotinic acid hydroxamate was achieved under these reaction conditions. To avoid substrate inhibition effect, a fed-batch process based on the optimized parameters with two feedings of substrates (200/200 mM) at 40-min intervals was developed and a molar conversion yield of 89.4 % with the productivity of 52.9 g h(-1) g (DCW) (-1) was achieved at laboratory scale. Finally, 6.4 g of powder containing 58.5 % (w/w) nicotinic acid hydroxamate was recovered after lyophilization and further purification resulted in 95 % pure product.

Effect of bioagents on cucumber seed mycoflora, seed germination, and seedling vigour.

The effect of different bioagents such as Trichoderma harzianum, T. viride, T. virens, Pseudomonas fluorescens, and Bacillus subtilis was studied on seed mycoflora, seed germination, root/shoot length, and seedling vigour of cucumber var. Solan Srijan under in vitro conditions. Alternaria sp., Aspergillus sp., and Fusarium spp. were observed on cucumber as seed mycoflora, with T. harzianum showing the greatest inhibition for Alternaria sp. and Fusarium spp., and T. viride showing the greatest inhibition for Aspergillus sp. Cucumber var. Solan Srijan seeds were treated with various bio agents, with T. harzianum being the most effective in increasing seed germination (88.75%), root length (13.58 cm), shoot length (14.58 cm), and seedling vigour (2501.31).

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network.

The outbreak of COVID-19 has engulfed the entire world since the end of 2019, causing tremendous loss of lives. It has also taken a toll on the healthcare sector due to the inability to accurately predict the spread of disease as the arrangements for the essential supply of medical items largely depend on prior predictions. The objective of the study is to train a reliable model for predicting the spread of Coronavirus. The prediction capabilities of various powerful models such as the Autoregression Model (AR), Global Autoregression (GAR), Stacked-LSTM (Long Short-Term Memory), ARIMA (Autoregressive Integrated Moving Average), Facebook Prophet (FBProphet), and Residual Recurrent Neural Network (Res-RNN) were taken into consideration for predicting COVID-19 using the historical data of daily confirmed cases along with Twitter data. The COVID-19 prediction results attained from these models were not up to the mark. To enhance the prediction results, a novel model is proposed that utilizes the power of Res-RNN with some modifications. Gated Recurrent Unit (GRU) and LSTM units are also introduced in the model to handle the long-term dependencies. Neural Networks being data-hungry, a merged layer was added before the linear layer to combine tweet volume as additional features to reach data augmentation. The residual links are used to handle the overfitting problem. The proposed model RNN Convolutional Residual Network (RNNCON-Res) showcases dominating capability in country-level prediction 20 days ahead with respect to existing State-Of-The-Art (SOTA) methods. Sufficient experimentation was performed to analyze the prediction capability of different models. It was found that the proposed model RNNCON-Res has achieved 91% accuracy, which is better than all other existing models.

A review article on the dosimetry in intraluminal HDR brachytherapy of lung carcinoma.

Lung cancer is one of the most common cancers in the world. Intraluminal brachytherapy (BT) is one of the most adopted treatment modalities for lung malignancies with Ir-192 source in radiotherapy. In intraluminal BT, treatment delivery is required to be very accurate and precise with respect to the plan created in the treatment planning system (TPS). The BT dosimetry is necessary for better treatment outcomes. Therefore in this review article, some relevant studies were identified and analyzed for dosimetric outcomes in intraluminal BT in lung malignancies. The dosimetry in BT for plan verification is not presently in practice, which needs to be performed to check the variation between the planned and measured doses. The necessary dosimetric work done by the various researchers in intraluminal BT such as the Monte Carlo CYLTRAN code was used to calculate and measure the dose rate in any medium. Anthropomorphic phantom was used to measure doses at some distance from the source with Thermo luminescence dosimeters (TLDs). The dosimetric influence of air passage in the bronchus was evaluated with the GEANT4 Monte Carlo method. A pinpoint chamber was used to measure and quantify the impact of inhomogeneity in wax phantom for the Ir-192 source. The Gafchromic films and Monte Carlo methods were used to find the phantom and heterogeneities, which were found to underestimate the dose for the lungs and overestimated for the bones in TPS. The exact tool to quantify the variation in planned and delivered doses should be cost-effective and easy to use possibly with tissue equivalent phantoms and Gafchromic films in lung malignancies treatment.

Angle of concavity in planning target volume can be adopted as selection criteria for intensity-modulated radiation therapy or three-dimensional conformal radiotherapy technique in brain tumors.

Introduction: With innovation of medical imaging, radiotherapy attempts to conform the high dose region to the planning target volume (PTV). The present work aimed to assess the angle of concavity in PTV can be adopted as selection criteria for intensity-modulated radiation therapy (IMRT) or three-dimensional conformal radiotherapy (3DCRT) technique in Brain tumors. Materials and Methods: Thirty previously irradiated patients with brain tumors were replanned with both 3DCRT and IMRT technique. Angle of concavity (dip) in the PTV near the organs at risk was measured in the contoured structure set images of each patient. These cases were divided into three groups where angles were 0°, >120° and <120°. Dose of 60 Gy/30# was fixed. Results: In Group 1, the IMRT plan had better TV95% as compared to 3DCRT respectively with significant P value (P = 0.002). Mean of conformity index (CI) and Homogeneity Index (HI) were comparable. For Group 2 (angle >120°), the IMRT plan had better TV95% as compared to 3DCRT respectively with a significant P value (P = 0.021). HI and CI were not significant. For Group 3 (<120°), IMRT plan had better TV95% as compared to 3DCRT respectively with a significant P value (P = 0.001). HI and CI were better in IMRT arm with significant P value. Conclusion: The results from this study showed that the angle of concavity can be considered as an additional objective tool for selection criteria whether tumor can be treated with IMRT or 3DCRT. Tumors where angle of concavity was <120°, HI and CI provided more uniformity and conformity of dose distribution inside PTV with significant P values.

Optical coherence tomography image based eye disease detection using deep convolutional neural network.

Over the past few decades, health care industries and medical practitioners faced a lot of obstacles to diagnosing medical-related problems due to inadequate technology and availability of equipment. In the present era, computer science technologies such as IoT, Cloud Computing, Artificial Intelligence and its allied techniques, etc. play a crucial role in the identification of medical diseases, especially in the domain of Ophthalmology. Despite this, ophthalmologists have to perform the various disease diagnosis task manually which is time-consuming and the chances of error are also very high because some of the abnormalities of eye diseases possess the same symptoms. Furthermore, multiple autonomous systems also exist to categorize the diseases but their prediction rate does not accomplish state-of-art accuracy. In the proposed approach by implementing the concept of Attention, Transfer Learning with the Deep Convolution Neural Network, the model accomplished an accuracy of 97.79% and 95.6% on the training and testing data respectively. This autonomous model efficiently classifies the various oscular disorders namely Choroidal Neovascularization, Diabetic Macular Edema, Drusen from the Optical Coherence Tomography images. It may provide a realistic solution to the healthcare sector to bring down the ophthalmologist burden in the screening of Diabetic Retinopathy.

Evaluation of metastatic neck nodes search for primary and subsequent management.

Background: Cancers in the head-and-neck region are the sixth most common cancers worldwide with an increasing incidence in developing countries. Methods: This study was carried out in the department of ENT and head-and-neck surgery in collaboration with the department of radiotherapy for a period of 1 year extending from May 01, 2014, to April 30, 2015. A total of 48 cases of metastatic secondary nodes were included in the study. Results: The male-to-female ratio in the present study was 4.33:1. Maximum number of patients were seen in the seventh decade. The youngest patient was a female 30 years old and the oldest was a male of 80 years. About 95.84% of primary tumors were squamous cell carcinomas and 40.47% of the patients of head-and-neck cancer with metastatic lymph nodes had well-differentiated squamous cell carcinomas. Majority of cases presented with N2 nodes, while N1 nodes were highest in cases of carcinomas larynx. Conclusions: Metastatic neck disease is a major problem in patients with head-and-neck cancer. The therapeutic goal includes not only known disease but also the elimination of possible subclinical disease. The judicious use of moderate doses of irradiation and modified surgical procedures should be used in specific clinical situations to significantly decrease neck recurrences while eliminating morbidity.

Predicting risk of obesity and meal planning to reduce the obese in adulthood using artificial intelligence.

BACKGROUND: An unhealthy diet or excessive amount of food intake creates obesity issues in human beings that further may cause several diseases such as Polycystic Ovary Syndrome (PCOS), Cardiovascular disease, Diabetes, Cancers, etc. Obesity is a major risk factor for PCOS, which is a common disease in women and is significantly correlated with weight gain. METHODS: This study is providing a one-step solution for predicting the risk of obesity using different Machine Learning (ML) algorithms such as Gradient Boosting (GB), Bagging meta-estimator (BME), XG Boost (XGB), Random Forest (RF), Support Vector Machine (SVM), and K Nearest Neighbour (KNN). A dataset is collected from the UCI ML repository having features of physical description and eating habits of individuals to train the proposed model. RESULTS: The model has been experimented with different training and testing data ratios such as (90:10, 80:20, 70:30,60:40). At a data ratio of 90:10, the GB classifier achieved the highest accuracy i.e., 98.11%. Further, at the 80:20 ratio, the GB and XGB provide the same result i.e., 97.87%. For the 70:30 data ratio, XGB achieves the highest accuracy i.e., 97.79%. Further, the Nearest Neighbour (NN) learning method is applied to meal planning to overcome obesity. CONCLUSION: This method predicts the meal which includes breakfast, morning snacks, lunch, evening snacks, and dinner for the individual as per caloric and macronutrient requirements. The proposed research work can be used by practitioners to check obesity levels and to suggest meals to reduce the obese in adulthood.