Hybrid morphological-convolutional neural networks for computer-aided diagnosis.

ABSTRACT: Training deep Convolutional Neural Networks (CNNs) presents challenges in terms of memory requirements and computational resources, often resulting in issues such as model overfitting and lack of generalization. These challenges can only be mitigated by using an excessive number of training images. However, medical image datasets commonly suffer from data scarcity due to the complexities involved in their acquisition, preparation, and curation. To address this issue, we propose a compact and hybrid machine learning architecture based on the Morphological and Convolutional Neural Network (MCNN), followed by a Random Forest classifier. Unlike deep CNN architectures, the MCNN was specifically designed to achieve effective performance with medical image datasets limited to a few hundred sample

SUBMITTER: Canales-Fiscal MR 

PROVIDER: S-EPMC10546173 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature