Abstract: Disease detection using deep learning networks and Residual Networks (ResNets) has revolutionized medical imaging and diagnostics, providing unprecedented accuracy and efficiency in analyzing medical images such as X-rays, MRIs, and CT scans.Deep learning, with their unique architecture comprising convolutional, pooling, and fully connected layers, excel in feature extraction, making them highly effective in detecting various diseases including pneumonia, breast cancer, diabetic retinopathy, and skin cancer. These networks apply filters to input images to detect features like edges, textures, and shapes, while pooling layers reduce the spatial dimensions, retaining essential features and reducing computational load. ResNets, an advanced form , address the vanishing gradient problem by introducing residual blocks that allow gradients to flow through the network more easily, enabling the training of much deeper networks. This capability is crucial for accurate disease detection, particularly in complex tasks like tumor identification and classification of intricate diseases. The residual blocks include identity mappings that bypass one or more layers, thus facilitating the development of very deep networks that perform better than their shallower counterparts.
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DOI:
10.17148/IJARCCE.2024.13609
