Computer Aided Diagnosis of Brain Tumour Using Walrus Optimization Algorithm

被引:0
作者
Kumar, S. Selvin Prem [1 ]
Kumar, C. Agees [2 ]
机构
[1] CSI Inst Technol, Dept Comp Sci & Engn, Thovalai, Tamil Nadu, India
[2] Arunachala Coll Engn Women, Dept EEE, Nagercoil, Tamil Nadu, India
关键词
Computer-Aided Diagnosis (CAD); Walrus Optimization Algorithm (WOA); Deep Learning (DL); Magnetic Resonance Imaging (MRI); Long short-term memory (LSTM); CLASSIFICATION; MRI;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Brain tumour segmentation is one of the most challenging problems in medical image analysis. To detect brain cancers, radiologists must use a computer-based tumour classification model. In medical imaging research, several computer-aided diagnostic (CAD) models are available to help radiologists with their patients. The reason for brain growth division is to deliver exact outline of brain tumour regions. This study suggests a Walrus Optimisation Algorithm approach for detecting brain tumours using MRI (Magnetic Resonance Imaging). Separating the characteristics into four categories has been utilized: no growth, gliomas, meningiomas, and pituitary cancers. The deep learning based model inception AlexNet and ResNet-18 is trained on an augmented training dataset. The CNN classifier is used for characteristics map improvement, while the LSTM (Long momentary memory) classifier is utilized for order. Besides, the boundary remembered for the classifiers is chosen aimlessly utilizing the Walrus Improvement Calculation to build the presentation of the CNN-LSTM classifier. The performance of the tumour diagnosis is assessed using the metrics: overall classification accuracy of 98.8%, precision of 96.23%, recall of 97.01%, specificity of 98%.
引用
收藏
页码:2111 / 2126
页数:16
相关论文
共 24 条
[1]  
Alhothali A, 2023, INT J ADV COMPUT SC, V14, P257
[2]  
Alqudah Ali Mohammad, 2020, arXiv
[3]  
Aly R. H. M., 2019, Procedia Computer Science, V163, P165
[4]  
[Anonymous], 2019, CAD system for brain tumor classification using MATLAB-based feature extraction and support vector machine
[5]   Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images [J].
Asif, Sohaib ;
Yi, Wenhui ;
Ul Ain, Qurrat ;
Hou, Jin ;
Yi, Tao ;
Si, Jinhai .
IEEE ACCESS, 2022, 10 :34716-34730
[6]  
Brindha P. Gokila, 2021, IOP Conference Series: Materials Science and Engineering, V1055, DOI 10.1088/1757-899X/1055/1/012115
[7]   A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network [J].
Diaz-Pernas, Francisco Javier ;
Martinez-Zarzuela, Mario ;
Anton-Rodriguez, Miriam ;
Gonzalez-Ortega, David .
HEALTHCARE, 2021, 9 (02)
[8]   Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm [J].
El-Dahshan, El-Sayed A. ;
Mohsen, Heba M. ;
Revett, Kenneth ;
Salem, Abdel-Badeeh M. .
EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (11) :5526-5545
[9]   Brain tumors classification with deep learning using data augmentation [J].
Gurkahraman, Kali ;
Karakis, Rukiye .
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2021, 36 (02) :997-1011
[10]  
Henry T, 2020, Brain tumor segmentation with selfensembled, deeply-supervised 3d u-net neural networks: a brats 2020 challenge solution