Early tumor diagnosis in brain MR images via deep convolutional neural network model

Tapan Kumar Das, Pradeep Kumar Roy, Mohy Uddin, Kathiravan Srinivasan, Chuan Yu Chang, Shabbir Syed-Abdul

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)2413-2429
Number of pages17
JournalComputers, Materials and Continua
Volume68
Issue number2
DOIs
Publication statusPublished - Apr 13 2021

Keywords

  • Brain tumor magnetic resonance imaging
  • Convolutional neural network
  • Deep learning

ASJC Scopus subject areas

  • Biomaterials
  • Modelling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering

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