Cepstrum-Based Texture Features for Melanoma Detection

2025/08/31 by Keith Miller, Miller, Keith, Jason Hagerty +6
Computer Science · Medicine · Neuroscience · #AI in cancer detection #Brain Tumor Detection and Classification #Cutaneous Melanoma Detection and Management #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2509.00669

openalex publication_date 2025/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper introduces a set of cepstrum-based texture features for melanoma classification and validates their performance on dermoscopic images from the ISIC 2019 dataset. We propose applying gray-level co-occurrence matrix (GLCM) statistics to 2D cepstral representations, a novel approach in image analysis. Combined with established handcrafted lesion descriptors, these features were evaluated using XGBoost models. Incorporating select cepstral features improved the area under the receiver operating characteristic curve, accuracy, and F1 score for binary melanoma vs. nevus classification. Results suggest that cepstral GLCM features offer complementary discriminatory information for melanoma detection.

Citations

• BCN20000: Dermoscopic Lesions in the Wild
• Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)
• Gray Level Co-Occurrence Matrices: Generalisation and Some New Features
• Textural Features for Image Classification

Related

• An Extensive Technique to Detect and Analyze Melanoma: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2017
• Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)
• Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks
• Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
• Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017\n International Symposium on Biomedical Imaging (ISBI), Hosted by the\n International Skin Imaging Collaboration (ISIC)