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.
Highlights
Automatic separation or segmentation of affected tissue from the healthy region is a difficult task due to the complicated structure and appearance of the tumor [1]
The training dataset includes 335 glioma patients with a high-grade glioma cases count of 259 cases and a low-grade glioma cases count of 76
The intratumor structures of edema, necrotic, enhancing tumor and nonenhancing tumor core have been clustered into regions of: The enhancing tumor region (ET) which contains entire tissues of tumor. (b) The tumor core region (TC) is used to enhance tumor, non-enhancing tumor core and necrotic. (c) The whole tumor region (WT)
Summary
Automatic separation or segmentation of affected tissue from the healthy region is a difficult task due to the complicated structure and appearance of the tumor [1]. Neural Networks (CNN) is a category of deep learning algorithm used by many researchers for segmentation and classification [3] It can learn the image by extracting features. It encouraged a proposal of a new segmentation and classification model with an optimized U-Net and hybrid classifier. Wang et al [9] have proposed a hybrid approach for brain tumor segmentation by combining CNNs into a bounding box and scribble-based segmentation pipeline. Some of the works proposed are based on optimizing hyperparameters of the learning model by metaheuristic algorithms. Wang et al [11] have proposed a particle swarm and genetic algorithm combined CNN for segmentation Both PSO and genetic developed to search optimal CNN parameters in order to provide a better learning rate.
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