Multi-modal medical image classification using deep residual network and genetic algorithm.

ABSTRACT: Artificial intelligence (AI) development across the health sector has recently been the most crucial. Early medical information, identification, diagnosis, classification, then analysis, along with viable remedies, are always beneficial developments. Precise and consistent image classification has critical in diagnosing and tactical decisions for healthcare. The core issue with image classification has become the semantic gap. Conventional machine learning algorithms for classification rely mainly on low-level but rather high-level characteristics, employ some handmade features to close the gap, but force intense feature extraction as well as classification approaches. Deep learning is a powerful tool with considerable advances in recent years, with deep convolution neural networks (CNNs)

SUBMITTER: Abid MH 

PROVIDER: S-EPMC10309999 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature