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

ABSTRACT:

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 u

SUBMITTER: Rios-Duarte JA 

PROVIDER: S-EPMC11091779 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature