Skin Lesion Diagnosis in Healthcare-Cyber Physical System

Abstract

Melanoma, one of the most deadly skin lesions is a major cause of death if not detected at earlier stages. Due to the high similarity between melanoma and other similar lesions, the investigation and diagnosis of these lesions are complex and time consuming job. To address such problems, in this paper, we present a machine learning integrated automated lesion classification in Healthcare Cyber-Physical System (HCPS) or smart healthcare framework. We propose an automated classification of various types of skin lesions by using deep learning tools, which save time, human effort, money, and human life by diagnosing melanoma at the earlier stage. Our model can classify 7 different types of skin lesions that are Melanocytic nevi, Melanoma, Benign keratosis, Basal cell carcinoma, Actinic keratoses, Vascular lesions, Dermatofibroma. The purpose of this paper is to classify the skin lesion with our pre-trained model. We have trained our model on multiple platforms like DenseNet169, DenseNet121, ResNet50, ResNet152 and found DenseNet 169 is most accurate among all. Therefore, the proposed technique may be useful for diagnosis of melanoma in rural places, where healthcare facilities, availability of oncologists and doctors are limited.