Decision Support System for Detection and Classification of Skin Cancer using CNN

TL;DR

This paper presents a CNN-based decision support system for early detection and classification of skin cancer using dermoscopy images, achieving high accuracy through image processing, augmentation, and transfer learning.

Contribution

It introduces a CNN model with transfer learning for skin cancer detection and classification, utilizing the HAM-10000 dataset to improve diagnostic accuracy.

Findings

CNN achieved 0.88 precision and 0.74 recall

Transfer learning with ResNet reached 90.51% accuracy

Image augmentation improved model robustness

Abstract

Skin Cancer is one of the most deathful of all the cancers. It is bound to spread to different parts of the body on the off chance that it is not analyzed and treated at the beginning time. It is mostly because of the abnormal growth of skin cells, often develops when the body is exposed to sunlight. The Detection Furthermore, the characterization of skin malignant growth in the beginning time is a costly and challenging procedure. It is classified where it develops and its cell type. High Precision and recall are required for the classification of lesions. The paper aims to use MNIST HAM-10000 dataset containing dermoscopy images. The objective is to propose a system that detects skin cancer and classifies it in different classes by using the Convolution Neural Network. The diagnosing methodology uses Image processing and deep learning model. The dermoscopy image of skin cancer taken, undergone various techniques to remove the noise and picture resolution. The image count is also increased by using various image augmentation techniques. In the end, the Transfer Learning method is used to increase the classification accuracy of the images further. Our CNN model gave a weighted average Precision of 0.88, a weighted Recall average of 0.74, and a weighted f1-score of 0.77. The transfer learning approach applied using ResNet model yielded an accuracy of 90.51%