Selected Technical Issues of Deep Neural Networks for Image Classification Purposes

Abstract

Malignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven datasets sizes, the huge intra-class variation with small interclass variation, and the existence of many artifacts in the image. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in a form of hand-crafted features. Automatic determination of the skin features used by the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of preprocessing the images. The system is an ensemble of ten neural networks, working in parallel and one network using their results to generate a final decision. This system structure allowed us to increase the efficiency of the operation by several percentage points compared to a single neural network. The proposed system is trained on over 5000 and tested afterward on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.