An Analysis of Deep Learning Techniques Adopted in Medical Imaging

Abstract

The various methods which have been adopted in processing and segmentation of medical images using deep learning and machine learning techniques are examined and analyzed in this article. Medical images analysis and their methods have been swiftly evolved into deep learning techniques and especially in convolutional neural networks. Deep learning concepts that may be used for the image classification, detection, and logging of medical related pictures and objects have been examined. Medical applications include: research and investigations into neuro, retinal and pulmonary, digital pathologies, breast, heart and musculoskeletal diseases and their corresponding analysis. Deep learning has already been used to accurately diagnose diseases and classify image samples, and it has the potential to revolutionise the entire landscape of healthcare. These uses are only expected to expand in the future. The most recent developments, including a critical analysis of the current problems have been summarized, and made plans for additional research in medical imaging.