Brain tumor recognition using an integrated bat algorithm with a convolutional neural network approach

被引：0

作者：

Chawla R. ^{[1
]}

Beram S.M. ^{[2
]}

Murthy C.R. ^{[3
]}

Thiruvenkadam T. ^{[4
]}

Bhavani N.P.G. ^{[5
]}

Saravanakumar R. ^{[6
]}

Sathishkumar P.J. ^{[7
]}

机构：

[1] Medical School, Akfa University, Tashkent

[2] Research Centre for Human-Machine Collaboration (HUMAC), Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Kuala Lumpur

[3] Electronics and Instrumentation Engineering, Sreevidyanikethan Engineering College, Tirupati

[4] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram

[5] Department of ECE, SIMATS School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamilnadu, Chennai

[6] Department of Wireless Communication, Institute of ECE, SIMATS School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai

[7] Dept of Computer Science and Engineering, Panimalar Engineering College

来源：

Measurement: Sensors | 2022年 / 24卷

关键词：

2-D gabor filter; Bat algorithm; Brain tumor; Classification; Convolutional neural network; MRI Images;

D O I：

10.1016/j.measen.2022.100426

中图分类号：

学科分类号：

摘要：

Tumors are uncontrolled growth of nerve cells that can lead to cancer.The ability to separate malignancies in the brain is critical for better diagnosis. As a result, there are a few research initiatives aimed at improving patient care. The manual method takes time and is only available in a few medical facilities. Treatment choices for brain cancer differ depending on the specific, shape, and position of the tumor, as well as your general health and preferences.Based on MRI input images, the system can automatically recognize a type of brain tumor. A Convolutional neural network and Bat algorithm are used in the proposed method to detect brain tumors in MRI images (B–CNN).To eliminate the noise the data is first pre-processed. To extract features from MRI brain pictures, the 2-D Gabor filter is utilized. For better accuracy, feature selection is based on the Bat algorithm.The data for this research were obtained from Nanfang Hospital and Tianjing Medical University's General Hospital.The proposed BCNN method results in a 99.5% accuracy rate when compared to the existing system. © 2022

引用

共 21 条

[1] Tandel G.S., Et al., A review on a deep learning perspective in brain cancer classification, Cancers, 11, 1, (2019)
[2] Gribko A., Et al., IsoMAG—an automated system for the immunomagnetic isolation of squamous cell carcinoma-derived circulating tumor cells, Diagnostics, 11, 11, (2021)
[3] Liu J., Li M., Wang J., Wu F., Liu T., Pan Y., A survey of MRI-based brain tumor segmentation methods, Tsinghua Sci. Technol., 19, 6, pp. 578-595, (2014)
[4] Sreekanth G.R., Fahad Alrasheedi A., Venkatachalam K., Abouhawwash M., Askar S.S., Extreme learning bat algorithm in brain tumor classification, Intell. Autom. Soft Comput., 34, 1, pp. 249-265, (2022)
[5] Swanson K.R., Bridge C., Murray J.D., Alvord E.C., Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion, J. Neurol. Sci., 216, 1, pp. 1-10, (2003)
[6] Mirjalili S., Mirjalili S.M., Yang X.-S., Binary bat algorithm, Neural Comput. Appl., 25, 3-4, pp. 663-681, (2014)
[7] Baranwal S.K., Jaiswal K., Vaibhav K., Kumar A., Srikantaswamy R., Performance analysis of brain tumour image classification using CNN and SVM, 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 537-542, (2020)
[8] Deepak S., Ameer P., Brain tumor classification using deep CNN features via transfer learning, Comput. Biol. Med., 111, (2019)
[9] Dey N., Rajinikanth V., Applications of Bat Algorithm and its Variants, (2021)
[10] Karthik R., Menaka R., Chellamuthu C., A comprehensive framework for classification of brain tumour images using SVM and curvelet transform, Int. J. Biomed. Eng. Technol., 17, 2, (2015)

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