Open Access

ARTICLE

Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

R. Rajaragavi^1,*, S. Palanivel Rajan²

1 Department of Information and Communication Engineering, Anna University, Chennai, 600025, India
2 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Thalavapalayam, Karur, 639113, India

* Corresponding Author: R. Rajaragavi. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 1-14. https://doi.org/10.32604/iasc.2022.021206

Received 26 June 2021; Accepted 27 July 2021; Issue published 26 October 2021

Abstract

A brain tumor is a portion of uneven cells, need to be detected earlier for treatment. Magnetic Resonance Imaging (MRI) is a routinely utilized procedure to take brain tumor images. Manual segmentation of tumor is a crucial task and laborious. There is a need for an automated system for segmentation and classification for tumor surgery and medical treatments. This work suggests an efficient brain tumor segmentation and classification based on deep learning techniques. Initially, Squirrel search optimized bidirectional ConvLSTM U-net with attention gate proposed for brain tumour segmentation. Then, the Hybrid Deep ResNet and Inception Model used for classification. Squirrel search optimizer mimics the searching behavior of southern flying squirrels and their well-organized way of movement. Here, the squirrel optimizer is utilized to tune the hyperparameters of the U-net model. In addition, bidirectional attention modules of position and channel modules were added in U-Net to extract more characteristic features. Implementation results on BraTS 2018 datasets show that proposed segmentation and classification outperforms in terms of accuracy, dice score, precision rate, recall rate, and Hausdorff Distance.

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