Author(s) :

Anuja S. B, F. Ramesh Dhanaseelan

Conference Name :

International Conference on Green Technology and Management for Environmental Sustainability (ICGMES-2024)

Abstract :

Diabetic Retinopathy (DR) is a leading cause of blindness worldwide. Early detection and accurate grading are crucial for effective treatment. This study proposes a computer aided diagnosis system using image processing techniques to detect and grade DR from retinal fundus images. The proposed approach involves image preprocessing and enhancement, followed by feature extraction using texture analysis and vessel segmentation. A novel hybrid technique, referred to as Hybrid GCAM (HGCAM), is introduced for feature extraction. This technique combines the Global Channel Attention Mechanism (GCAM) with multi scale feature refinement (MFR). It is mentioned here as Hybrid GCAM or HGCAM. GCAM Global channel attention mechanism will extract global features along with local features. It increases robustness, it reduces computational complexity by concentrating on critical features, GCAM enhances detection of microaneurysms, hemorrhages, and other subtle lesions. By integrating GCAM and multiscale feature refinement the model is supposed to improve diabetic retinopathy detection accuracy, enhance feature representation and robustness, reduce computational complexity and can gain insights into feature importance. The system uses Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) for classification. Early detection and treatment are critical to preventing vision loss, with multi label SVM classification (MLC) playing a key role in DR detection and grading. Here we propose a system to diagnose diabetic retinopathy (DR) from colored fundus images, enabling early detection and treatment. Our proposed deep learning based computer aided diagnosis (CAD) system detects and analyzes retinal pathological changes without invasive procedures. The system comprises: Image preprocessing (noise reduction, quality enhancement) Feature extraction and classification. Our system demonstrates promising results, outperforming existing methods. It enables accurate DR diagnosis, facilitating timely treatment and preventing complications.

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