Achievements of neural network in skin lesions classification

Abstract The gross mismatch of skin disease cases and the specialties to manage them is the main cause of a continuously increased disease burden. The skin disease burden contributes 1.79% toward the global disease burden. To lessen this burden, automated skin lesions classification schemes that can provide multiclass classification are highly demanded. This chapter presents an investigation into an automated classification scheme to classify multiple skin lesions (acne, eczema, psoriasis; benign, and malignant) using state-of-the-art machine learning techniques. In the proposed classification scheme, convolution neural network (CNN) is utilized using the transfer learning approach, and a pretrained CNN model “AlexNet” is used to retrain the classification model on the skin lesion dataset. The proposed classification scheme outperformed over existing classification schemes and obtained an accuracy of 96.65%. The multiclass classification scheme can be very beneficial in the limited resource areas as it can assist in the early diagnosis of multiple skin lesions.