Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks

• Published in: Skin research and technology Vol. 30; no. 5; pp. e13607 - n/a
• Main Authors: Rios‐Duarte, Jorge A., Diaz‐Valencia, Andres C., Combariza, Germán, Feles, Miguel, Peña‐Silva, Ricardo A.
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.05.2024; John Wiley and Sons Inc
• Subjects: artificial intelligence; Artificial neural networks; Classification; Deep Learning; Dermoscopy - methods; dermoscopy images; Feature extraction; Female; Humans; Image Interpretation, Computer-Assisted - methods; Machine learning; Male; Melanoma; Melanoma - classification; Melanoma - diagnostic imaging; Melanoma - pathology; Neural networks; Neural Networks, Computer; Original; ROC Curve; Sensitivity and Specificity; Skin cancer; Skin Neoplasms - classification; Skin Neoplasms - diagnostic imaging; Skin Neoplasms - pathology; Training; deep learning; dermoscopy images; skin cancer; melanoma; artificial intelligence
• ISSN: 0909-752X; 1600-0846
• DOI: 10.1111/srt.13607

Background Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion. Materials and Methods We divided 914 image pairs into training, validation, and test sets. Models were built using pre‐trained Inception‐ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC‐ROC. Results Significant AUC‐ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001). Conclusion CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN‐based melanoma classification appear less clear based on our findings.