Development of a Transfer Learning-Based, Multimodal Neural Network for Identifying Malignant Dermatological Lesions From Smartphone Images.

ABSTRACT:

Objectives

Early skin cancer detection in primary care settings is crucial for prognosis, yet clinicians often lack relevant training. Machine learning (ML) methods may offer a potential solution for this dilemma. This study aimed to develop a neural network for the binary classification of skin lesions into malignant and benign categories using smartphone images and clinical data via a multimodal and transfer learning-based approach.

Methods

We used the PAD-UFES-20 dataset, which included 2298 sets of lesion images. Three neural network models were developed: (1) a clinical data-based network, (2) an image-based network using a pre-trained DenseNet-121 and (3) a multimodal network combining clinical and image data. Models were tuned using Bayesian Optimisation HyperBand acr

SUBMITTER: Deng J 

PROVIDER: S-EPMC12188081 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature