Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy.

Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance of early detection in mitigating its impact. Addressing this challenge head-on, this study introduces an innovative deep learning framework tailored for DR diagnosis. The proposed framework utilizes the EfficientNetB0 architecture to classify diabetic retinopathy severity levels from retinal images.

Advance drought prediction through rainfall forecasting with hybrid deep learning model.

Drought is a natural disaster that can affect a larger area over time. Damage caused by the drought can only be reduced through its accurate prediction. In this context, we proposed a hybrid stacked model for rainfall prediction, which is crucial for effective drought forecasting and management.

EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data.

Background And Objectives: Early breast cancer subtypes classification improves the survival rate as it facilitates prognosis of the patient. In literature this problem was prominently solved by various Machine Learning and Deep Learning techniques. However, these studies have three major shortcomings: huge Trainable Weight Parameters (TWP), suffer from low performance and class imbalance problem.

Clinical evaluation of AYUSH-SL in patients receiving mass drug administration for the treatment of chronic inflammatory lymphedema, a double blind, placebo controlled, multicentric study.

Background Objectives: A 2.5-year placebo controlled double blind trial was conducted to investigate the safety and efficacy of AYUSH- SL, a poly- herbal Ayurvedic formulation on filarial lymphedema in different endemic areas of India. Lymphatic filariasis (LF) is caused by parasitic nematodes from Wuchereria bancrofti, Brugia malayi, or B.