Assessment of brain tumor detection techniques and recommendation of neural network

Sandeep Dwarkanath Pande; Shaik Hasane Ahammad; Boddapati Taraka Phan Madhav; Kalangi Ruth Ramya; Lassaad K Smirani; Md Amzad Hossain; Ahmed Nabih Zaki Rashed

doi:10.1515/bmt-2022-0336

Assessment of brain tumor detection techniques and recommendation of neural network

Biomed Tech (Berl). 2024 Jan 30. doi: 10.1515/bmt-2022-0336. Online ahead of print.

Authors

Sandeep Dwarkanath Pande¹, Shaik Hasane Ahammad², Boddapati Taraka Phan Madhav³, Kalangi Ruth Ramya³, Lassaad K Smirani⁴, Md Amzad Hossain⁵, Ahmed Nabih Zaki Rashed^{6

7}

Affiliations

¹ MIT Academy of Engineering, Pune, India.
² Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India.
³ Department of Computer Engineering, Indira College of Engineering and Management, Pune, MH, India.
⁴ Deanship of Information Technology, Umm Al-Qura University, Makkah, Saudi Arabia.
⁵ Department of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore, Bangladesh.
⁶ Electronics and Electrical Communications Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
⁷ Department of VLSI Microelectronics, Institute of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamilnadu, India.

PMID: 38285486
DOI: 10.1515/bmt-2022-0336

Abstract

Objectives: Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures for effective and efficient tumor detection. Here, the data of the multi-modal brain tumor segmentation task is employed, which has been registered, skull stripped, and histogram matching is conducted with the ferrous volume of high contrast.

Methods: This research further configures a capsule network (CapsNet) for brain tumor classification. Results of the latest deep neural network (NN) architectures for tumor detection are compared and presented. The VGG16 and CapsNet architectures yield the highest f1-score and precision values, followed by VGG19. Overall, ResNet152, MobileNet, and MobileNetV2 give us the lowest f1-score.

Results: The VGG16 and CapsNet have produced outstanding results. However, VGG16 and VGG19 are more profound architecture, resulting in slower computation speed. The research then recommends the latest suitable NN for effective brain tumor detection.

Conclusions: Finally, the work concludes with future directions and potential new architectures for tumor detection.

Keywords: brain tumor detection; capsule network; convolutional neural network; deep learning; machine learning.