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A STUDY ON DEEP LEARNING IN BIO-MEDICAL IMAGE PROCESSING

Niharika A, Dr. Prasanna Kumar S.C

Abstract


The goal of image fusion is to create a single image that is more instructive and useful for later applications by first extracting and then merging the most significant information from several source photos. Image fusion has advanced significantly as a result of deep learning, and the fused results are promising due to neural networks' strong feature extraction and reconstruction capabilities. Recent advances in deep learning technologies have led to a boom in picture fusion. But there isn't a thorough examination and critique of the most recent deep learning techniques in various fusion scenarios.In this study, we delve into the realm of deep learning in biomedical image processing, focusing on the application of techniques such as image fusion, feature extraction, modal image clustering, and the utilization of the Vgg19 neural network. By exploring these key aspects, we aim to highlight the significance of deep learning in enhancing the analysis and interpretation of various types of medical images, ultimately contributing to advancements in the field of healthcare and diagnostics.  


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