A Review on Novel Approach for Skin Cancer Detection

EasyChair Preprint 9846

Abstract

Skin cancer is the uncontrolled growth of abnormal cells in the epidermis (outermost skin layer) caused by damaged DNA that triggers mutations these mutations lead the skin cells to multiply rapidly and form malignant tumors. Diagnosis of an unknown skin lesion is crucial to enable proper treatments. While curable with early diagnosis, only highly trained dermatologists are capable of accurately recognize melanoma skin lesions. Expert dermatologist classification for melanoma dermoscopic images is 65-66%. As expertise is in limited supply, systems that can automatically classify skin lesions as either benign or malignant melanoma are very useful as initial screening tools. Towards this goal, this study presents a convolutional neural network model, trained on features extracted from a highway convolutional neural network pre-trained on dermoscopic images of skin lesions.

Keyphrases: CNN, Convolutional Neural Network, Melanoma Skin Cancer, Pooling layer, ReLU, Regression, Skin Cancer, computer vision, deep learning, detection, feature extraction, image processing, neural network

@booklet{EasyChair:9846,
  author    = {M. Gaikwad and Vinay Keswani and Rushikesh Zope and Harsha Khadgi and Parag Gautam and Pragati Wankhede and Himani Hedaoo},
  title     = {A Review on Novel Approach for Skin Cancer Detection},
  howpublished = {EasyChair Preprint 9846},
  year      = {EasyChair, 2023}}