Skin Cancer Detection Using Deep Learning

Skin Cancer Detection Using Deep Learning

Nutan Chaudhari 1,*   , Sakshi More 2     , Virendra Gaikwad 3           Amruta Saraf4

1Department of Computer Engineering, Matoshri College of Engineering Nashik, Savitribai phule pune University

2Department of Computer Engineering, Matoshri College of Engineering Nashik, Savitribai phule pune University

3Department of Computer Engineering, Matoshri College of Engineering Nashik, Savitribai phule pune University

4Department of Computer Engineering, Matoshri College of Engineering Nashik, Savitribai phule pune University

Abstract: In cancer, there are over 200 different forms. Out of 200, melanoma is the deadliest form of skin cancer. The diagnostic procedure for melanoma starts with clinical screening, followed by dermoscopic analysis and histopathological examination. Melanoma skin cancer is highly curable if it gets identified at the early stages. The first step of Melanoma skin cancer diagnosis is to conduct a visual examination of the skin's affected area. Dermatologists take the dermatoscopic images of the skin lesions by the high-speed camera, which have an accuracy of 65-80% in the melanoma diagnosis without any additional technical support. With further visual examination by cancer treatment specialists and dermatoscopic images, the overall prediction rate of melanoma diagnosis raised to 75-84% accuracy. The project aims to build an automated classification system based on image processing techniques to classify skin cancer using skin lesions images.

Keywords: deep learning; machine learning; convolutional neural network; HAM10000; skin le- sion; ESRGAN