A Comparative Study of 2D and 3D Convolutional Neural Networks for Melanoma Classification

Skin Melanoma is a lethal type of cancer. The early diagnosis of which is crucial to improve the survival rate of the patients. Convolution neural networks are at the heart of the deep learning algorithms. In the present work authors have experimentally compared 2D and 3D Convolution Neural Network (CNN) models to identify the melanoma. We have employed three different types of datasets namely PH2, ISIC archive, and ISIC skin cancer datasets. We applied the two models on each of the datasets to determine their accuracy, precision, recall, f1 score and ROC curves. The experimental results provide the insights about the advantages and limitations of using 2D and 3D CNN models for the identification of skin melanoma. The authors have observed that 2D CNN model shows enhanced capabilities to detect skin lesion structures compared to 3D CNN. Moreover, the classification accuracy of the 2D CNN is also found better than 3D CNN.