Heart Attack Risk Prediction Using Retinal Eye Images Based on Machine Learning and Image Processing
Keywords:
Heart attack, retinal image, U-Net, Efficient Net, deep learning, classification, segmentation, prediction, medical imaging, diagnosisAbstract
Early diagnosis of heart attacks is a critical challenge in the healthcare industry, as early diagnosis tends to result in significantly better patient outcomes. Retinal imaging has been proven to be an efficient and non-invasive diagnostic tool for cardiovascular disease, as the retina provides vascular changes indicative of heart disease. Conventional machine learning techniques, including Support Vector Machines (SVM) and Random Forests, have been applied to address this task., their efficiency is usually undermined by the heterogeneity and complexity of image data and their incapability of extracting useful features. To counter these, a deep learning model is proposed that integrates U-Net architecture has been employed for segmenting retinal images. Efficient Net for classification. U-Net up-scales the input images by outlining prominent features such as blood vessels and eliminating background noise. The up-scaled images are then subjected to Efficient Net, which is best suited for detection of complex patterns due to its optimized and scalable architecture. This integrated process enhances the model's sensitivity to detect subtle indicators of cardiovascular risk easily missed by traditional models. The system therefore provides enhanced prediction accuracy and facilitates a quicker, automated, and non-invasive diagnostic process. This makes it an effective tool for supporting early diagnosis and preventive care of cardiovascular diseases
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