Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network

TL;DR

This paper presents a CNN-based system that uses intensity value estimation to improve the accuracy of melanoma and nevus mole classification, achieving over 92% accuracy in experiments.

Contribution

The study introduces a novel approach that incorporates high-intensity pixel features into CNN for better skin cancer detection accuracy.

Findings

Achieved 92.58% accuracy in melanoma classification

Sensitivity of 93.76% indicates high true positive rate

Specificity of 91.56% demonstrates effective true negative detection

Abstract

Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance of the proposed model than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), sensitivity (93.76%), specificity (91.56%), and precision (90.68%) are achieved.