Journal Article

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

Ashraf, Rehan, Faisal, C. M. Nadeem, Abid, Muhammad Haris, Mahmood, Toqeer

2023

Overview

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) succeeding in image classification. The main goal is to bridge the semantic gap and enhance the classification performance of multi-modal medical images based on the deep learning-based model ResNet50. The data set included 28378 multi-modal medical images to train and validate the model. Overall accuracy, precision, recall, and F1-score evaluation parameters have been calculated. The proposed model classifies medical images more accurately than other state-of-the-art methods. The intended research experiment attained an accuracy level of 98.61%. The suggested study directly benefits the health service.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Analysis / Artificial Intelligence / Artificial neural networks / Biology and Life Sciences / Classification / Computer and Information Sciences / COVID-19 / Decision making / Deep learning / Evaluation / Feature extraction / Genetic algorithms / Health Facilities / Health services / Illnesses / Image classification / Image databases / Image enhancement / Image processing / Image retrieval / Investigations / Machine Learning / Medical imaging / Medicine and Health Sciences / Mental Recall / Model accuracy / Neural networks / Physical Sciences / Research and Analysis Methods / Semantics / Social Sciences / Tactics