Convolutional Neural Network Based Desktop Applications to Classify Dermatological Diseases

Convolutional Neural Networks (CNNs) have the potential to assist medical doctors in diagnosis and treatment stage. This paper has been prepared to help dermatologists by presenting (i) fundamental information on deep learning and CNNs, and (ii) applications of CNN s for skin diseases classification. Also, this work shows that although CNN based methods have a strong potential for automated diagnosis, further researches and new techniques are still required in image processing and pattern recognition area to provide diagnosis of dermatological diseases with higher performances. In this work, these two groups of applications of CNN s in dermatology have been handled: (i) disease classification from medical images (e.g., dermoscopy and pathological images); (ii) disease classification from digital photographs. Therefore, important contributions of this work are two-fold: First, main concepts of deep learning and CNNs are presented, which will be helpful for dermatologists to understand and follow up CNN based computerized methods. Second, the state-of-the-art applications developed for lesion classifications from medical images and colored photographs are presented. Also, disadvantages or limitations of these applications are explained. In addition, this paper indicates shortage of desktop applications developed for other dermatological diseases except skin cancer.