Classification of Skin Cancer Images using Convolutional Neural Networks

TL;DR

This paper explores the use of convolutional neural networks with transfer learning to classify skin cancer images, achieving over 86.65% accuracy on a publicly available dataset, demonstrating the potential of deep learning in medical image diagnosis.

Contribution

It introduces a CNN-based approach with transfer learning for skin cancer image classification, achieving high accuracy and utilizing a publicly available dataset for reproducibility.

Findings

Achieved over 86.65% accuracy in skin cancer image classification

Demonstrated the effectiveness of transfer learning with CNNs for medical images

Utilized a publicly available dataset from ISIC for reproducible research

Abstract

Skin cancer is the most common human malignancy(American Cancer Society) which is primarily diagnosed visually, starting with an initial clinical screening and followed potentially by dermoscopic(related to skin) analysis, a biopsy and histopathological examination. Skin cancer occurs when errors (mutations) occur in the DNA of skin cells. The mutations cause the cells to grow out of control and form a mass of cancer cells. The aim of this study was to try to classify images of skin lesions with the help of convolutional neural networks. The deep neural networks show humongous potential for image classification while taking into account the large variability exhibited by the environment. Here we trained images based on the pixel values and classified them on the basis of disease labels. The dataset was acquired from an Open Source Kaggle Repository(Kaggle Dataset)which itself was acquired from ISIC(International Skin Imaging Collaboration) Archive. The training was performed on multiple models accompanied with Transfer Learning. The highest model accuracy achieved was over 86.65%. The dataset used is publicly available to ensure credibility and reproducibility of the aforementioned result.