Prediction of Cardiovascular Diseases with Retinal Images Using Deep Learning

CVDs) represent a formidable global health challenge, necessitating astute early detection and intervention strategies. This study introduces a sophisticated deep learning paradigm, seamlessly integrating the discriminative prowess of convolutional neural networks (CNNs) with the computational efficiency intrinsic to MobileNet architecture, aiming to discern CVDs from retinal images with unprecedented accuracy. The dataset, meticulously curated to encompass diverse clinical scenarios, undergoes scrupulous preprocessing, augmenting the model’s robustness. The amalgamation of CNN architecture and streamlined MobileNet design culminates a discerning framework capable of intricate classification tasks related to CVD presence within retinal imagery. This paradigm shift holds promise for expeditious CVD diagnosis, risk stratification, and economically viable medical imaging and envisions a transformative era wherein healthcare professionals are endowed with timely insights, facilitating judicious interventions and proactive healthcare strategies. Nevertheless, the imperatives of rigorous validation and seamless integration into clinical practice loom, their fruition being pivotal to gauging the veritable clinical utility and transformative potential of this innovative synthesis of cutting-edge retinal imaging and deep learning technologies in cardiovascular health.