Title: Biomedical signal to image conversion and classification using flexible deep learning techniques

Authors: Abhishek Das; Soumya Ranjan Nayak; Mihir Narayan Mohanty; Piyush Kumar Shukla

Addresses: Department of Electronics and Communication Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India ' Department of Electronics and Communication Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (State Technological University of Madhya Pradesh), Bhopal, Madhya Pradesh, India

Abstract: Most diseases are diagnosed from image data like CT scans, MRIs, and X-rays. Gene data carries vital information related to the diseases that needs to be analysed for diagnosis. Both the logics are combined and a flexible deep ensemble learning-based model is proposed for the classification of images generated from one-dimensional (1D) data. Earlier works in the detection of brain tumours and epileptic seizures have been developed either directly providing 1D data or images to the classification model, whereas the proposed method utilises the effectiveness of two-dimensional (2D) convolutional neural networks (CNNS) to analyse 1D data like gene expressions and EEG signals after effective conversion to images. The data conversion is performed using three data reduction techniques, i.e., locally linearly embedding (LL-Embedding), multi-dimensional scaling (MDS), and t-distributed stochastic neighbour embedding (t-SNE) with convex hull algorithm to wrap all the data points. Multilayer perceptron is used for second-stage training. The proposed method is verified using brain tumour gene data collected from the genomic data commons (GDC) data portal and the EEG data for epileptic seizures detection provided by the University of Bonn (UoB dataset) and provided 97.38% and 97.33% accuracies respectively.

Keywords: gene to image; EEG to image; convolution neural network; ensemble learning; multilayer perception; brain tumour; epileptic; seizure; convex hull.

DOI: 10.1504/IJBIDM.2024.136439

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.2, pp.183 - 202

Received: 29 Jan 2022
Accepted: 31 Oct 2022
Published online: 01 Feb 2024 *

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