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Retinal Detachment (RD) is one of the major problems with retinal disorder patients. Till to date there existing no confirmatory sign or marker on retina for the early detection of RD. Therefore, patients may have sudden RD at any time of their life. Moreover, it is completely dependent upon the subjective judgement of ophthalmologist to make the final diagnostic decision on RD. To support the decision making process for the ophthalmologist, in this article we proposed RDNet, a SqueezeNet architecture based deep learning model for the early detection of RD. We used publicly available dataset of 1017 images covering rhegmatogenous RD and control group. The proposed model built on this image set achieved 97.55% sensitivity, 99.26% specificity and 98.23% accuracy in detecting RD. The proposed model outperformed the existing models for the same purpose with the highest area under the ROC curve (AUC) of 0.995. We believe our model will support the early detection of RD in clinical setup and assist the ophthalmologist in identifying RD at its early stage.
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Lung Adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two main histology subtypes of non-small cell lung cancer (NSCLC) with 70% of total Lung Cancer. In this article we proposed an ensemble-based model for the identification of subtypes of NSCLC using methylation data. Proposed Random Forest-based model along with out of bag (OOB) error based feature selection technique identified the top ten most important CpG sites that are highly differentiator between LUSC and LUAD subtypes of NSCLC with an accuracy, precision and F1 Score of \(97\%\) . The proposed model outperformed the other existing models for the same purpose with huge margin of 12%. Pathway analysis of the proposed 10 CpG sites revealed different pathways for LUAD and LUSC associated genes, LUAD-associated genes primarily participated in TP53, PTEN, GLP-1, Incretin regulation, and apoptosis. Conversely, LUSC-associated genes were predominantly involved in pathways for platelet degranulation, serine biosynthesis, and Nephrin family interaction.
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Diabetes, affecting more than 500 million individuals worldwide, is the most widespread non-communicable disease, globally. The early identification and effective management of diabetes are crucial for controlling its spread. Currently, the HbA1c test is the gold standard for the detection of diabetes with high confidence. But this is an invasive, expensive pathology test. Therefore, alternative non-invasive and inexpensive methods have been proposed in the literature for the early detection of diabetes.
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