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) 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.

A Preliminary Study Assessing a Transfer Learning Approach to Intestinal Image Analysis to Help Determine Treatment Response in Canine Protein-Losing Enteropathy

“…Through the use of algorithms, the machine is able to adapt its models to improve its ability to make predictions. Image recognition algorithms are specifically trained to classify images based on their content [ 2 ]. Machine learning used in image processing is usually performed using a convolutional neural network approach known as deep learning [ 3 ].…”

Using Generative Adversarial Networks and Ensemble Learning for Multi-Modal Medical Image Fusion to Improve the Diagnosis of Rare Neurological Disorders

“…According to similar research, numerous medical CBIR approaches have recently been introduced. Most advanced CBIR recovery techniques utilize just one kind of illumination [4]. One method to find the necessary healthcare images over huge collections of images is to employ resemblance contrast, and extraction techniques can allow the user to select the image category first.…”