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Medical Image Analysis Using Machine Learning and Deep Learning: A Comprehensive Review
Noakhali Science and Technology University, Noakhali, Bangladesh.ORCID iD: 0000-0002-4279-0878
University of Chittagong University-4331, Chittagong, Bangladesh.ORCID iD: 0000-0002-3090-7645
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2022 (English)In: Rhythms in Healthcare / [ed] M. Shamim Kaiser; Mufti Mahmud; Shamim Al Mamun, Springer Nature, 2022, p. 147-161Chapter in book (Other academic)
Abstract [en]

Medical imaging is essential in a variety of medical applications, like medical treatments had been used for early identification, tracking, prognosis, and diagnosis testing of different medical problems. Machine learning is critical in the field of image processing and computer vision. Machine learning (ML) techniques are used to analyze information from visual data in problems ranging from image segmentation and registration to formation, object classification, and scene understanding. Deep learning (DL) is being used to analyze medical images, which is a growing field. DL methodologies and their applications to computer-aided diagnosis involve standard machine learning techniques in the field of computer vision, deep learning ML models, and applications to medical image processing. Many of the most recent machine learning technologies in computer-aided diagnosis and medical image processing are the classification of objects such as lesions into specific classes associated with the input attributes such as contrast and area acquired from segmented object classes. Theoretically, an artificial neural network is influenced by neural structures. The Neocognitron, CNNs, and neural filters all are significant deep learning techniques. Image-based machine learning, which includes deep learning, is a useful and high-performing technology. In the upcoming years, deep learning will become the standard technology for medical image analysis. We present a review of recent machine learning and deep learning approaches for detecting four diseases, including tuberculosis, lung cancer, pneumonia, and COVID-19, in this study. We review the disease which are detected and classified from X-ray images. We intend to explore the most accurate technique for detecting various diseases as part of this study, which will be useful in the future.

Place, publisher, year, edition, pages
Springer Nature, 2022. p. 147-161
Series
Studies in Rhythm Engineering, ISSN 2524-5546, E-ISSN 2524-5554
Keywords [en]
Medical image, X-ray, Tuberculosis, Lung cancer, COVID-19, Pneumonia
National Category
Computer Sciences Other Clinical Medicine
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-93413DOI: 10.1007/978-981-19-4189-4_10OAI: oai:DiVA.org:ltu-93413DiVA, id: diva2:1700988
Note

ISBN för värdpublikation: 978-981-19-4188-7 (print), 978-981-19-4189-4 (digital)

Available from: 2022-10-04 Created: 2022-10-04 Last updated: 2022-10-04Bibliographically approved

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Andersson, Karl

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