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Research Article |

Detection and Classification of MRI Brain Tumors using S3-DRLSTM Based Deep Learning Model

Author(s): E. Aarthi1, S. Jana2, W. Gracy Theresa3, M. Krishnamurthy4, A. S. Prakaash5, C. Senthilkumar6 and S. Gopalakrishnan7

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 3

Publisher : FOREX Publication

Published : 15 September 2022

e-ISSN : 2347-470X

Page(s) : 597-603

DOI: https://doi.org/10.37391/IJEER.100331

Abstract

Developing an automated brain tumor diagnosis system is a highly challenging task in current days, due to the complex structure of nervous system. The Magnetic Resonance Imaging (MRIs) are extensively used by the medical experts for earlier disease identification and diagnosis. In the conventional works, the different types of medical image processing techniques are developed for designing an automated tumor detection system. Still, it remains with the problems of reduced learning rate, complexity in mathematical operations, and high time consumption for training. Therefore, the proposed work intends to implement a novel segmentation-based classification system for developing an automated brain tumor detection system. In this framework, a Convoluted Gaussian Filtering (CGF) technique is used for normalizing the medical images by eliminating the noise artifacts. Then, the Sparse Space Segmentation (S3) algorithm is implemented for segmenting the pre-processed image into the non-overlapping regions. Moreover, the multi-feature extraction model is used for extracting the contrast, correlation, mean, and entropy features from the segmented portions. The Deep Recurrent Long-Short Term Memory (DRLSTM) technique is utilized for predicting the classified label as normal of disease affected. During results analysis, the performance of the proposed system is tested and compared by using various evaluation measures.

Keywords: Brain Tumor Detection, Magnetic Resonance Imaging (MRI), Convoluted Gaussian Filtering (CGF), Sparse Space Segmentation (S3), Multi-Feature Extraction, Deep Recurrent Long Short-Term Memory (DRLSTM) Classification




E. Aarthi, Assistant Professor, Department of Computer Science, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India; Email: aarthi.devpal@gmail.com

S. Jana, Professor, Department of Electronics and Communication Engineering, Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India; Email: seljana.1995@gmail.com

W. Gracy Theresa, Associate Professor, Department of Computer and Science Engineering, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India; Email: sunphin14@gmail.com

M. Krishnamurthy, Assistant Professor, Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Kothandaraman Nagar, Tamil Nadu 624622, India; Email: krishnamurthy184@gmail.com

A. S. Prakaash, Associate Professor, Department of Mathematics, Panimalar Engineering College, Chennai, Tamil Nadu, India; Email: prakaashphd333@gmail.com

C. Senthilkumar, Assistant Professor (S.G), Department of Electronics and Communication Engineering, Saveetha School of Engineering (Saveetha University), Chennai, Tamilnadu-602 105, India; Email: senswain@gmail.com

S. Gopalakrishnan, Professor, Department of Electronics and Communication Engineering, Siddhartha Institute of Technology and Sciences, Narapally, Hyderabad, Telangana-500088.India; Email: drsgk85@gmail.com

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E. Aarthi, S. Jana, W. Gracy Theresa, M. Krishnamurthy, A. S. Prakaash, C. Senthilkumar, S. Gopalakrishnan (2022), Detection and Classification of MRI Brain Tumors using S3-DRLSTM Based Deep Learning Model. IJEER 10(3), 597-603. DOI: 10.37391/IJEER.100331.