A Comprehensive assessment of Convolutional Neural Networks for skin and oral cancer detection using medical images

Abstract

Early detection is essential to effectively treat two of the most prevalent cancers, skin and oral. Deep learning approaches have demonstrated promising results in effectively detecting these cancers using Computer-Aided Cancer Detection (CAD) and medical imagery. This study proposes a deep learning-based method for detecting skin and oral cancer using medical images. We discuss various Convolutional Neural Network (CNN) models such as AlexNet, VGGNet, Inception, ResNet, DenseNet, and Graph Neural Network (GNN). Image processing techniques such as image resizing and image filtering are applied to skin cancer and oral cancer images to improve the quality and remove noise from images. Data augmentation techniques are used next to expand the training dataset and strengthen the robustness of the CNN model. The best CNN model is selected based on the training accuracy, training loss, validation accuracy, and validation loss. The study shows DenseNet achieves state-of-the-art performance on the skin cancer dataset.

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