Open Access

ARTICLE

Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

by Tapan Kumar Das¹, Pradeep Kumar Roy², Mohy Uddin³, Kathiravan Srinivasan^1,*, Chuan-Yu Chang^4,*, Shabbir Syed-Abdul⁵

1 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
2 Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, 395007, India
3 Research Quality Management Section, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard - Health Affairs, Riyadh, 11426, Kingdom of Saudi Arabia
4 Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
5 Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan

* Corresponding Author: Chuan-Yu Chang. Email:

(This article belongs to the Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2021, 68(2), 2413-2429. https://doi.org/10.32604/cmc.2021.016698

Received 08 January 2021; Accepted 08 February 2021; Issue published 13 April 2021

Abstract

Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high accuracy. This research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the tumor. The system runs on Tensor flow and uses a feature extraction module in Deep-CNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR image. The results of this study showed that our model did not have any adverse effect on classification, achieved higher accuracy than the peers in recent years, and attained good detection outcomes including case of abnormality. In the future work, further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.

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