Disease Diagnosis Systems Using Machine Learning and Deep learning Techniques Based on TensorFlow Toolkit: A review

A.Alsalman, Firdews; Khorshid, Shler Farhad; Sallow, Amira Bibo

doi:10.33899/csmj.2022.174415

	Disease Diagnosis Systems Using Machine Learning and Deep learning Techniques Based on TensorFlow Toolkit: A review
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 14, Volume 16, Issue 1, 2022, Pages 111-120 PDF (988.22 K)
Document Type: Review Paper
DOI: 10.33899/csmj.2022.174415
Authors
Firdews A.Alsalman^* ¹; Shler Farhad Khorshid²; Amira Bibo Sallow³
¹College of Computer Science and Mathematics Department of Software University of Mosul
²Akre Technical College of Informatics Department of Information Technology Duhok Polytechnic University
³Technical College of Administration Department of Information Technology Management Duhok Polytechnic University
Abstract
Machine learning and deep learning algorithms have become increasingly important in the medical field, especially for diagnosing disease using medical databases. Techniques developed within these two fields are now used to classify different diseases. Although the number of Machine Learning algorithms is vast and increasing, the number of frameworks and libraries that implement them is also vast and growing. TensorFlow is a well-known machine learning library that has been used by several researchers in the field of disease classification. With the help of TensorFlow (Google's framework), a complex calculation can be addressed effectively by modeling it as a graph and properly mapping the graph segments to the machine in the form of a cluster. In this review paper, the role of the TensorFlow-Python framework- for disease classification is discussed.
Keywords
Machine learning; Deep learning; TensorFlow; Python; Disease Diagnosis

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