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Research Article |

MRI Intracranial Neoplasm Localization Using Convolution Neural Network Based Residual Block ResUnet: A Systematic Approach

Author(s): Dr. G N Keshava Murthy*, Dr. Sujata Joshi, Dr. Rekha H, Dr. Usha B. S and Nandini Prasad K. S

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 4

Publisher : FOREX Publication

Published : 19 December 2023

e-ISSN : 2347-470X

Page(s) : 1128-1135

DOI: https://doi.org/10.37391/ijeer.110433

Abstract

Analysis of intracranial neoplasm using multimodal MR images requires accurate and automatic segmentation. However, manually classifying tumors with similar structures or appearances in magnetic resonance imaging (MRI) with similar anatomy or appearances is more challenging, requiring experience to detect brain tumors. Precise segmentation of brain tumors gives clinicians with a foundation for surgical planning and treatment. Due to its capacity to segment brain tumor images automatically, Deep Neural Networks (DNN) have been widely used in image segmentation applications. To classify, segment and marking the occurrence of the brain tumor area accurately, we present custom Deep Convolution Neural Network (CNN) based Residual block U-Net (RB-ResUnet) architecture. Our technique is tested on publicly available Kaggle datasets utilizing quantitative metrics. Comparative results demonstrate that the custom CNN-based RB-ResUnet model can more reliably identify tumor locations and give accurate segmentation masks to tumor locations that are defined by bounding boxes. The findings of the experiment reveal that our proposed model RB-ResUnet can effectively aid in the identification, toxicological evaluation of the brain tumor and has clinical research as well as practical application.

Keywords: Deep learning, Convolution Neural Network, ResU-Net, Residual block, Brain Tumor Segmentation.




Dr. G N Keshava Murthy*, Assistant Professor, Department of EIE, Siddaganga Institute of Technology, Tumkur, India; Email: keshavamurthytg5@gmail.com

Dr. Sujata Joshi, Associate Professor, Department of CSE, Nitte Meenakshi Institute of Technology, Bangalore, India

Dr. Rekha H, Professor, Department of ISE, Shridevi Institute of Engineering and Technology, Tumkur, India

Dr. Usha B. S, Professor, Department of CSE, East West Institute of Technology, Bangalore, India

Nandini Prasad K. S, Professor, Department of ISE, Dayananda Sagar Academy of Technology and Management, Bangalore, India

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Dr. G N Keshava Murthy, Dr. Sujata Joshi, Dr. Rekha H, Dr. Usha B. S, Nandini Prasad K. S (2023), MRI Intracranial Neoplasm Localization Using Convolution Neural Network Based Residual Block ResUnet: A Systematic Approach. IJEER 11(4), 1128-1135. DOI: 10.37391/ijeer.110433.