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

arXiv: https://arxiv.org/abs/2209.15465 · HTML: https://mlsanity.com/paper/2209.15465/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 53 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Decision Support System for Detection and Classification of Skin Cancer using CNN](https://mlsanity.com/paper/1912.03798/) (2019)
- [Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study](https://mlsanity.com/paper/1708.05081/) (2017)

## Abstract (arXiv)

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.