Design of Intelligent Technique for Abnormality Detection in MRI Brain Images
Farha Anjum Mansoori1, Agya Mishra2
1Farha Anjum Mansoori, Department of Electronics and Telecommunication Engineering, Jabalpur Engineering College, Jabalpur (M.P), India.
2Dr. Agya Mishra, Department of Electronics and Telecommunication Engineering, Jabalpur Engineering College, Jabalpur (M.P), India.
Manuscript received on 30 December 2022 | Revised Manuscript received on 07 January 2023 | Manuscript Accepted on 15 January 2023 | Manuscript published on 30 January 2023 | PP: 77-85 | Volume-11 Issue-5, January 2023 | Retrieval Number: 100.1/ijrte.E74330111523 | DOI: 10.35940/ijrte.E7433.0111523
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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: This paper presents an intelligent technique particularly for MRI brain images. This introduces a clever method designed specifically for MRI brain pictures. To determine the abnormality in the brain images is processed using intelligent hybrid method of convolution neural networks and curvelet transform. Feature extraction, the logistic regression method (LRM), and learning algorithms are all used in the proposed model. Additionally, the categorization system identifies cancerous or non-cancerous tumours in the images of the brain. Results from experiments demonstrate how well model- and parameter-based analysis performs. The topic of minimum batch accuracy and validation accuracy, which are then contrasted with the current method, comes to a conclusion in the paper. This concept is suited to ongoing MRI image analysis activities. In this paper, previous paper has also be reviewed and their method is investigated.
Keywords: CNN, MRI Brain Image, Curvelet Transform, Brain Cancer, Transfer Learning, Logistic Regression Model.
Scope of the Article: CNN