Integrating Color Histogram Analysis and Convolutional Neural Network for Skin Lesion Classification

TL;DR

This paper combines color histogram analysis with a deep CNN to classify skin lesions, emphasizing the number of colors as a key diagnostic feature and achieving a weighted F1 score of 75%.

Contribution

It introduces a novel approach that integrates color histogram features with CNN classification, highlighting the importance of lesion color diversity for diagnosis.

Findings

Number of colors is a significant diagnostic feature.

Proposed CNN achieves a weighted F1 score of 75%.

Color histogram analysis effectively estimates lesion color ground truth.

Abstract

The color of skin lesions is an important diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue gray. This study introduces a novel feature: the number of colors present in a lesion, which can indicate the severity of disease and help distinguish melanomas from benign lesions. We propose a color histogram analysis method to examine lesion pixel values from three publicly available datasets: PH2, ISIC2016, and Med Node. The PH2 dataset contains ground truth annotations of lesion colors, while ISIC2016 and Med Node do not; our algorithm estimates the ground truth using color histogram analysis based on PH2. We then design and train a 19 layer Convolutional Neural Network (CNN) with residual skip connections to classify lesions into three categories based on the number of colors present. DeepDream visualization is used to interpret features learned by the network, and multiple CNN configurations are tested. The best model achieves a weighted F1 score of 75 percent. LIME is applied to identify important regions influencing model decisions. The results show that the number of colors in a lesion is a significant feature for describing skin conditions, and the proposed CNN with three skip connections demonstrates strong potential for clinical diagnostic support.