A Deep Learning-Based Approach for Classifying Heart Disease Using CNN on Medical Imaging

Heart disease detection from medical images such as X-rays, CT scans, and MRIs is a critical task in the healthcare industry. In this proposed work, we explore the application of Convolutional Neural Networks (CNNs) for the classification of heart disease from these medical images. CNNs are highly effective in image-based tasks due to their ability to learn hierarchical features from raw image pixels. The proposed methodology includes the collection of medical image datasets, such as Chest X-ray and Cardiovascular Disease datasets, which are labeled based on the presence or absence of heart disease. These images are preprocessed by resizing, normalizing, and augmenting the dataset to improve model generalization. The CNN model consists of multiple convolutional layers that detect low-level features, followed by max-pooling layers to reduce dimensionality. Fully connected layers are used to combine the extracted features and make predictions, with the output layer using a sigmoid activation function for binary classification. The model is trained using the Adam optimizer and evaluated based on accuracy, precision, recall, Fl-score, and confusion matrix. The results demonstrate that the CNN-based approach outperforms traditional machine learning algorithms like SVM and Random Forest in heart disease detection, with better classification metrics and lower false positive/negative rates. Future improvements could include using advanced CNN architectures, such as VGGNet or InceptionV3, and integrating ECG signals for more accurate predictions. This work highlights the potential of CNNs for heart disease detection and lays the foundation for future research in deep learning applications for healthcare.