Skin Cancer Diagnosis With Multi-Level Classification

Abstract

Skin cancer arises from the uncontrolled proliferation of abnormal skin cells, primarily triggered by exposure to the harmful ultraviolet (UV) rays of the sun and the utilization of UV tanning beds. This condition poses a heightened risk due to its potential to progress into blood cancer and lead to rapid fatality. Extensive research efforts have been dedicated to advancing the treatment of this perilous ailment. This paper presents a system designed for the examination and diagnosis of pigmented skin lesions and melanoma. The system incorporates a supervised classification algorithm that combines Convolutional Neural Network (CNN) and Deep Neural Network (DNN) architectures with feature extraction techniques. It operates in two distinct stages: the initial stage classifies images into two categories, namely benign or malignant, while the subsequent stage further categorizes the images into one of three classes: basal cell carcinomas, squamous cell carcinomas, or melanoma. Consequently, the comprehensive system addresses four classes, namely benign, basal cell carcinomas, squamous cell carcinomas, and melanoma. This work contributes to the system's design in three significant ways. Firstly, it implements multiple iterations to select the most optimal images, resulting in the highest classification accuracy. Secondly, it employs various statistical methods to identify the most pertinent features, thereby enhancing the classifier's accuracy by focusing on the most informative features for the classification task. Lastly, a two-stage classification approach is implemented, employing two distinct classifiers at different levels within the overall system. Despite the inherent complexity of the real-world problem, the overall system attains a commendable level of classification accuracy. Following rigorous experimentation, the study identifies the top three models. Each approach culminates in a classifier for each stage. The first approach, utilizing a deep learning classifier, achieves an accuracy of 81.82% in the initial cancer discrimination stage and 58.33% in the subsequent stage. The second approach, employing a machine learning classifier, attains an accuracy of 74.63% in the first stage and 64.41% in the second stage. The third approach, utilizing a linear regression classifier, achieves an accuracy of 98% in the first stage and 90% in the second stage. These results underscore the significance of feature selection in influencing model accuracy and suggest the potential for further optimization.