Impact of Deep Learning in Medical Imaging: a Systematic New Proposed Model
Surayya Ado Bala1, Shri Kant2, Khemendra Kumar3
1Surayya Ado Bala, Department of Computer Science and Engineering, Sharda University, Greater Noida (U.P), India.
2Shri Kant, Department of Computer Science and Engineering, Sharda University, Greater Noida (U.P), India.
3Khemendra Kumar, Department of Radio Diagnosis, Sharda Hospital, Greater Noida (U.P), India.
Manuscript received on 15 November 2019 | Revised Manuscript received on 04 December 2019 | Manuscript Published on 10 December 2019 | PP: 112-118 | Volume-8 Issue-3S2 October 2019 | Retrieval Number: C10191083S219/2019©BEIESP | DOI: 10.35940/ijrte.C1019.1083S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: For years’ radiologist and clinician continues to employs various approaches, machine learning algorithms included to detect, diagnose, and prevent diseases using medical imaging. Recent advances in deep learning made medical imaging analysis and processing an active research area, various algorithms for segmentation, detection, and classification have been proposed. In this survey, we describe the trends of deep learning algorithms use in medical imaging, their architecture, hardware, and software used are all discussed. We concluded with the proposed model for brain lesion segmentation and classification using Magnetic Resonance Images (MRI).
Keywords: Deep Learning Architectures, Data Augmentation Machine Learning, Medical Imaging.
Scope of the Article: Deep Learning