Medical image classification and retrieval using deep learning

Abstract

Medical image classification and retrieval using deep learning is an innovative application of artificial intelligence in the field of medical imaging. This technique involves training deep neural networks to classify and retrieve medical images based on their content [1]. There are many techniques available nowadays which seems more genuine and useful, for example, artificial neural networks in the field of medical imaging. These techniques are simply having more ability to perform the variety of operations like classification data visualization image segmentations based on tumours active or inactive lesions.In the medical image classification first, we trained our model according to the given information and data. When we trained our data, we were able to classify the new medical images based on this trained model and make predictions. This method is very useful in monitoring and planning the medical treatments and diagnosis of humans.The prediction and report generation is a difficult task in medical imaging. The creation of a deep learning data model for predictions has many challenges like data availability, data effectiveness, and its integrity. The results generated from the analysis of medical images possess great risk with its implementation to the real world. However, the deep learning techniques improve a lot in the outcome and prediction model in the field of medical imaging to improve patient outcomes.