Harnessing Deep Learning for Early Detection of Cardiovascular Diseases

Cardiovascular diseases (CVDs) rank among the top causes of death globally. Timely detection and accurate diagnosis of CVDs are essential for effective treatment and better patient outcomes. Retinal imaging has emerged as a cost-effective and minimally invasive method for predicting CVDs. This study aims to develop a deep learning model that employs convolutional neural networks (CNNs) and the MobileNet architecture to predict cardiovascular diseases from retinal images. The model harnesses the power of CNNs to autonomously extract significant features from retinal images while utilizing MobileNet's lightweight framework for efficient implementation. A comprehensive dataset comprising retinal images from both healthy individuals and CVD patients is utilized for training and evaluating the model. Preprocessing steps, including resizing, normalization, and data augmentation, are applied to improve the quality and variety of the dataset. This model has the potential to aid healthcare professionals in making informed decisions, facilitating timely interventions and preventive healthcare measures. Further validation and integration into clinical settings are necessary to thoroughly evaluate its effectiveness and influence on patient care.