Deep residual network based brain tumor segmentation and detection with MRI using improved invasive bat algorithm

被引:6
|
作者
Gupta, Vimal [1 ]
Bibhu, Vimal [2 ]
机构
[1] JSS Acad Tech Educ, Comp Sci & Engn, C-20-1,Sect 62, Noida 201301, Uttar Pradesh, India
[2] Amity Univ, Comp Sci & Engn, Plot 48 A,Knowledge Pk 3, Greater Noida 201308, Uttar Pradesh, India
关键词
Deep learning; Brain tumor detection; Data augmentation; Generative Adversarial Network (GAN); Magnetic resonance imaging (MRI); CLASSIFICATION; DESIGN; FUSION;
D O I
10.1007/s11042-022-13769-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A brain tumor is the mass of abnormal and unnecessary cells growing in the brain and it is also considered a life-threatening disease. Hence, segmentation and detection of such tumors at an early stage with Magnetic Resonance Image (MRI) is more significant to save the life. MRI is very effective to find persons with brain cancer such that the detection rate of this modality is moderately higher rather than considering other imaging modalities. Due to the size, shape, and appearance variations, the detection of brain tumors is a major complex task in the system of medical imaging. Hence, an efficient brain tumor detection technique is designed using the proposed Improved Invasive Bat (IIB)-based Deep Residual network model. Accordingly, the proposed IIB algorithm is derived by incorporating the Improved Invasive Weed Optimization (IWO) and Bat algorithm (BA), respectively. The segmentation of tumors with MR images has a great impact on detecting the brain tumor at the beginning stage. The deep learning-based method effectively generated better detection results with MR images. With segmentation results, features are acquired from the tumor regions that are further used to make the detection process with the Deep Residual network. However, the proposed method achieved higher performance in terms of the measures, such as accuracy, sensitivity, and specificity by computing the values of 0.9256, 0.9003, and 0.9146, respectively.
引用
收藏
页码:12445 / 12467
页数:23
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