Daily Archives: March 3, 2026

Authors: Priti Birajdar, Ambika Kshirsagar, Shravani Raut, Harshada Raykar, Prajakta Bhadale

Abstract: This paper presents an Artificial Intelligence (AI) based system designed for the early detection of brain-related diseases such as Alzheimer's disease, Parkinson's disease, brain tumors, and stroke using medical imaging and machine learning techniques. Early diagnosis of neurological disorders is critical for effective treatment and improved patient outcomes. Traditional diagnostic approaches rely heavily on manual interpretation of MRI scans, which may lead to delayed detection and human error. The proposed system integrates Deep Learning models, particularly Convolutional Neural Networks (CNN), to analyze MRI images and detect abnormalities at an early stage. The architecture consists of image preprocessing, feature extraction, classification, and result visualization modules. The system aims to assist neurologists by providing accurate and fast predictions.

Published by: vikaspatanker

Authors: Ch.Srilakshmi, Nithish Kanth M, Rupesh J, Tharun CR

Abstract: Retinal vessel segmentation is essential for the early diagnosis of diseases such as diabetic retinopathy, hypertensive retinopathy, and age-related macular degeneration. Manual segmentation of fundus images is time-consuming and prone to variability, limiting large-scale screening. This paper presents RETINASEG, a deep learning-based system for automated pixel-level segmentation of retinal vessels from fundus images. The proposed framework combines image enhancement techniques such as contrast normalization, CLAHE, and noise reduction with an encoder–decoder architecture based on U-Net and transformer-enhanced models. To address challenges including thin vessel detection and class imbalance, data augmentation and class-balanced loss functions are employed during training. Experimental results on DRIVE and STARE datasets demonstrate strong performance, achieving high accuracy and robustness across datasets. A web-based interface with real-time visualization and explainable AI support further enhances clinical usability. RETINASEG enables scalable, reliable, and automated retinal analysis for early disease detection and tele-ophthalmology applications.

Published by: vikaspatanker