An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features.

ABSTRACT: Melanoma is the most lethal of all skin cancers. This necessitates the need for a machine learning-driven skin cancer detection system to help medical professionals with early detection. We propose an integrated multi-modal ensemble framework that combines deep convolution neural representations with extracted lesion characteristics and patient meta-data. This study intends to integrate transfer-learned image features, global and local textural information, and patient data using a custom generator to diagnose skin cancer accurately. The architecture combines multiple models in a weighted ensemble strategy, which was trained and validated on specific and distinct datasets, namely, HAM10000, BCN20000 + MSK, and the ISIC2020 challenge datasets. They were evaluated on the mean values of preci

SUBMITTER: Sharafudeen M 

PROVIDER: S-EPMC10406737 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature