Abstract
The prevalence of melanoma skin cancer has increased in recent decades. The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins. Thus, the early diagnosis of melanoma is a key factor in improving the prognosis of the disease. Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images. Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases. This paper proposes a new method which can be used for both skin lesion segmentation and classification problems. This solution makes use of Convolutional neural networks (CNN) with the architecture two-dimensional (Conv2D) using three phases: feature extraction, classification and detection. The proposed method is mainly designed for skin cancer detection and diagnosis. Using the public dataset International Skin Imaging Collaboration (ISIC), the impact of the proposed segmentation method on the performance of the classification accuracy was investigated. The obtained results showed that the proposed skin cancer detection and classification method had a good performance with an accuracy of 94%, sensitivity of 92% and specificity of 96%. Also comparing with the related work using the same dataset, i.e., ISIC, showed a better performance of the proposed method.