Precision Eye Care Employing YOLO V7 for the Detection of Optical Glaucoma via Python

Project Synopsis & Overview

The project aims to develop an automated optical glaucoma detection system for identifying and categorizing glaucoma severity from retinal fundus images. The proposed system addresses the limitations of conventional glaucoma screening methods, including manual feature extraction, limited sensitivity and specificity, lack of real-time capability, and difficulty in handling complex retinal image patterns. The system utilizes the YOLOv7 deep learning architecture for real-time object detection and glaucoma severity categorization across four glaucoma severity classes. The workflow includes data collection, image preprocessing, model implementation, model training, and output generation. Preprocessing involves image normalization, resizing, and data augmentation techniques such as rotation, flipping, and scaling, along with handling missing or corrupted data and maintaining balanced class distributions. The model implementation integrates YOLOv7 and uses transfer learning and model fine-tuning for the glaucoma detection task. The output module applies the trained model to unseen retinal images and generates detected glaucomatous regions using bounding boxes with associated probability scores. The system is intended for automated glaucoma screening, clinical decision support, telemedicine, large-scale screening, and medical image analysis through a Flask-based web application.

Complete Technical Specifications