Title: Internet of medical things and cloud enabled brain tumour diagnosis model using deep learning with kernel extreme learning machine

Authors: M. Ganesan; N. Sivakumar; M. Thirumaran; T. Vengattaraman

Addresses: Department of CSE, Sri Manakula Vinayagar Engineering College, Puducherry, India ' Department of CSE, Pondicherry Engineering College, Puducherry, India ' Department of CSE, Pondicherry Engineering College, Puducherry, India ' Department of Computer Science, Pondicherry University, Puducherry, India

Abstract: Presently, internet of things (IoT) and cloud-based e-health services offer various decision support systems in the medical field. In this view, this paper introduces a new internet of medical things (IoMT) and cloud-enabled brain tumour (BT) diagnosis and classification using deep learning-based inception model with the kernel extreme learning machine (KELM), named DLIM-KELM. The proposed DLIM-KELM undergoes a series of steps namely data acquisition, pre-processing, optimal multi-level threshold-based segmentation, Inception v3-based feature extraction, and KELM-based classification. Besides, firefly (FF) algorithm is applied for the selection of optimal threshold value in Tsallis entropy-based segmentation technique. The application of Inception v3 and KELM models helps to effectively diagnose and classify the occurrence of BT from magnetic resonance imaging (MRI) images. The DLIM-KELM model is tested using the BRATS2015 dataset and it has attained maximum sensitivity of 98.45%, specificity of 98.34%, and accuracy of 98.91%.

Keywords: internet of medical things; IoMT; cloud computing; e-healthcare; electronic healthcare; deep learning; feature extraction.

DOI: 10.1504/IJEH.2022.124490

International Journal of Electronic Healthcare, 2022 Vol.12 No.3, pp.203 - 220

Accepted: 18 Dec 2021
Published online: 27 Jul 2022 *

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