Open Access

ARTICLE

Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

G. Ignisha Rajathi¹, R. Ramesh Kumar², D. Ravikumar³, T. Joel⁴, Seifedine Kadry^4,5, Chang-Won Jeong⁶, Yunyoung Nam^7,*

1 Department of Computer Science and Business Systems, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
2 Department of Information Technology, Sri Krishna College of Technology, Coimbatore, 641008, India
3 Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, 602117, India
4 Department of Electronics and Communication Engineering, R.M.K. Engineering College, Chennai, 601206, India
5 Deparmtent of Applied Data Science, Noroff University College, Kristiansand, Norway
6 Medical Convergence Research Center, Wonkwang University, Iksan, Korea
7 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea

* Corresponding Author: Yunyoung Nam. Email:

Computer Systems Science and Engineering 2023, 44(2), 1793-1806. https://doi.org/10.32604/csse.2023.024674

Received 27 October 2021; Accepted 31 December 2021; Issue published 15 June 2022

Abstract

Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging (MRI) brain images and transmit them to the cloud server. Besides, adaptive window filtering (AWF) based image preprocessing is used to remove noise. In addition, the cloud server executes the disease diagnosis model which includes the sparrow search algorithm (SSA) with GoogleNet (SSA-GN) model. The IoMTC-HDBT model applies functional link neural network (FLNN), which has the ability to detect and classify the MRI brain images as normal or abnormal. It finds useful to generate the reports instantly for patients located in remote areas. The validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is carried out interms of sensitivity, accuracy, and specificity. The experimentation outcome pointed out the betterment of the proposed model with the accuracy of 0.984.

---

Keywords

---