Abstract
Medical is a sector all different from other sectors, it is always the highest demanding sector. The days we look back, medical experts are the one who can process medical images such as X-rays etc. Deep learning nowadays has involved deeply into medical sector. If the processing or analyzing of medical image is concerned, it mostly varies according to the knowledge of the analyzer. Thus to avoid this variation and get more accurate results in the analysis of medical images, deep learning can play a major role. This paper discusses about the use of deep learning for medical processing. This paper aims to provide an introduction of deep learning in the medical sector for image processing. Firstly, the process of image processing neural network has been discussed and then a review of machine learning architectures about CNN, learning with CNN, RNN, Boltzmann machine is presented. The application of neural networks in medical image analysis has also been discussed such as detection, segmentation, registration and localization etc. This paper is concluded by adding some points about challenges and future applications of deep learning networks. The advantage of deep learning in terms of accuracy over machine learning and artificial intelligence has also been presented. Although, the machine learning algorithms are able to provide a great knowledge and information about medical image analysis because it has so efficient and accurate algorithms which are able to interact with image detection.
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Sharma, A.K., Nandal, A., Dhaka, A., Dixit, R. (2021). Medical Image Classification Techniques and Analysis Using Deep Learning Networks: A Review. In: Patgiri, R., Biswas, A., Roy, P. (eds) Health Informatics: A Computational Perspective in Healthcare. Studies in Computational Intelligence, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-15-9735-0_13
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DOI: https://doi.org/10.1007/978-981-15-9735-0_13
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