Deep Learning in Medical Image Analysis Article Review

Media Ali Ibrahim; shavan askar; Mohammad  saleem; Daban Ali; Nihad  Abdullah

doi:10.33022/ijcs.v13i2.3842

Authors

Media Ali Ibrahim
Prof. Dr. Shavan Askar Erbil Polytechnic University
Mohammad saleem Erbil Polytechnic University
Daban Ali Erbil Polytechnic University
Nihad Abdullah Erbil Polytechnic University

DOI:

https://doi.org/10.33022/ijcs.v13i2.3842

Keywords:

Artificial Intelligence (AI), Deep Leaning, Image Classification

Abstract

Transfer learning, in evaluation to common deep studying strategies which include convolutional neural networks (CNNs), stands proud due to its simplicity, efficiency, and coffee education value, efficaciously addressing the venture of restricted datasets. The importance of scientific picture analysis in both scientific research and medical prognosis can't be overstated, with image techniques like Computer Tomography (CT), Magnetic Resonance Image (MRI), Ultrasound (US), and X-Ray playing a crucial function. Despite their utility in non-invasive analysis, the scarcity of categorized medical images poses a completely unique challenge in comparison to datasets in other pc imaginative and prescient domains, like facial reputation.

Given this shortage, switch getting to know has won reputation amongst researchers for medical photo processing. This complete evaluation draws on one hundred amazing papers from IEEE, Elsevier, Google Scholar, Web of Science, and diverse sources spanning 2000 to 2023 It covers vital components, which includes the (i) shape of CNNs, (ii) foundational know-how of switch learning, (iii) numerous techniques for enforcing transfer mastering, (iv) the utility of switch gaining knowledge of throughout numerous sub-fields of medical photo analysis, and (v) a dialogue at the future potentialities of transfer studying within the realm of medical image analysis. This evaluate no longer handiest equips beginners with a scientific understanding of transfer mastering applications in medical image analysis but additionally serves policymakers by means of summarizing the evolving trends in transfer learning within the scientific image domain. This insight might also encourage policymakers to formulate advantageous rules that support the continued development of Transfer learning knowledge of in medical image analysis.

Author Biography

Prof. Dr. Shavan Askar, Erbil Polytechnic University

Dr. Shavan Askar (Professor of Computer Networks since 15/3/2023). He received his PhD degree in Electronic Systems Engineering from the University of Essex\UK in 2012. He obtained his MSc (2003) and BSc (2001, Ranked 1st on the college) degrees from the Control and Systems Engineering Dept. Baghdad. Dr. Askar works in the field of Networks that includes Internet of Things, Software Defined Networks, Optical Networks, and 5G. Dr. Askar has started his academic career in 2003 when he was appointed as a lecturer at the University of Duhok Iraq until 2008 when he was granted a scholarship to do his PhD degree that commenced in October 2008 and finished successfully in June 2012. Dr. Askar then returned to Iraq to pursue his academic career at the University of Duhok for the period 2012-2016 by supervising master students, teaching post-gradatue courses, and became project manager of so many strategic projects in Kurdistan. In 2016, Dr. Askar joined Duhok Polytechnic University as the Director General of Scientific Research Center, his role includes apart from teaching post-graduate students, contributing to the development of the university from the technological and scientific perspectives. Since 2017, Dr. Askar beside his DPU job is working as an Adjunct Professor at the American University of Kurdistan, he contributed into the establishment of a new program called Electronic and Telecommunications Engineering\College of Engineering, he teaches different courses in this program. Dr. Askar has more than 95 scientific research papers, some of his papers were published in very prestigious conferences such as OFC and ECOC and high impact factor journals. While he was in UK, he worked as a Researcher in two European projects; MAINS project (Metro Architecture enabling Sub wavelengths) and ADDONAS project (Active Distributed and Dynamic Optical Network Access Systems).