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Conventional and Non-Conventional ANNs in Medical Diagnostics: A Tutorial Survey of Architectures, Algorithms, and Application

Conventional and Non-Conventional ANNs in Medical Diagnostics: A Tutorial Survey of Architectures, Algorithms, and Application

Devika G., Asha G. Karegowda
Copyright: © 2021 |Pages: 39
ISBN13: 9781799850717|ISBN10: 1799850714|EISBN13: 9781799850724
DOI: 10.4018/978-1-7998-5071-7.ch001
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MLA

G., Devika, and Asha G. Karegowda. "Conventional and Non-Conventional ANNs in Medical Diagnostics: A Tutorial Survey of Architectures, Algorithms, and Application." Deep Learning Applications in Medical Imaging, edited by Sanjay Saxena and Sudip Paul, IGI Global, 2021, pp. 1-39. https://doi.org/10.4018/978-1-7998-5071-7.ch001

APA

G., D. & Karegowda, A. G. (2021). Conventional and Non-Conventional ANNs in Medical Diagnostics: A Tutorial Survey of Architectures, Algorithms, and Application. In S. Saxena & S. Paul (Eds.), Deep Learning Applications in Medical Imaging (pp. 1-39). IGI Global. https://doi.org/10.4018/978-1-7998-5071-7.ch001

Chicago

G., Devika, and Asha G. Karegowda. "Conventional and Non-Conventional ANNs in Medical Diagnostics: A Tutorial Survey of Architectures, Algorithms, and Application." In Deep Learning Applications in Medical Imaging, edited by Sanjay Saxena and Sudip Paul, 1-39. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5071-7.ch001

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Abstract

Computer technology advancements in recent days have offered professionals in different fields the ability to gather data, process information, store, and retrieve at a faster rate and make effective decisions. The large collection of data among all various applications including medical diagnosis has paved the need to employ advanced artificial neural networks (ANN). This chapter provides a detailed working view of ANN, covering its various architectures and design techniques in brief. A detailed analysis and summary of medical diagnostics applications using various ANN techniques will be leveraged. Imbalanced data is the major problem with medical data. This chapter briefs on the various methods to handle imbalanced data. Finally, future directions and potential current challenges are suggested for additional applications in neural networks.

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