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An improved computer based diagnosis system for early detection of abnormal lesions in the brain tissues with using magnetic resonance and computerized tomography images

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Abstract

Detection of masses can be a challenging task for radiologists and physicians. Manual tumor diagnosis in the brain is sometimes a time consuming process and can be insufficient for fast and accurate detection and interpretation. This study introduces an improved interface-supported early diagnosis system to increase the speed and accuracy for supporting the traditional methods. The first stage in the system involves collecting information from the brain tissue, and assessing whether it is normal or abnormal through the processing of Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT) images. The next stage involves gathering results from the image(s) after the single/multiple and volumetric and multiscale image analysis. The other stage involves Feature Extraction for some cases and making an interpretation about the abnormal Region of Interest (ROI) area via Deep Learning and conventional Artificial Intelligence methods is the last stage. The output of the system is mainly the name of the mass type which was introduced to the network. The results were obtained for totally 300 images for High-Grade Glioma (HGG), Low-Grade Glioma (LGG), Glioblastoma (GBM), Meningioma as well as Ischemic and Hemorrhagic stroke. For the cases, the DICE score was obtained as 0.927 and the normal/abnormal differentiation of the brain tissues was also achieved successfully. Finally, this system can give a chance to the doctors for supporting the results, speeding up the diagnosis process and also decreasing the rate of possible misdiagnosis.

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Abbreviations

MRI :

Magnetic Resonance Imaging.

CT :

Computerized Tomography.

ROI :

Region Of Interest.

GBM :

Glioblastoma.

HGG :

High Grade Glioma.

LGG :

Low Grade Glioma.

DW-MRI :

Diffusion-Weighted Magnetic Resonance Imaging.

DICOM :

Digital Imaging and Communications in Medicine.

NIfTI :

Neuroimaging Informatics Technology Initiative.

CAD :

Computer Aided Diagnosis.

GUI :

Graphical User Interface.

CNN :

Convolutional Neural Network.

VGG :

Visual Geometry Group.

∇I :

Difference between the Gaussian image and blurred image.

φ(i) :

Surface at iteration of i.

Wa :

Advection weight, and Wc and Fc represent the curvature weight and force.

Fa :

Force value.

Wc :

Curvature weight.

Wc :

Curvature force.

SVM :

Support Vector Machine

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Acknowledgments

In addition, Mehmet KOCABAY (BSc Student from Gazi University Electrical Electronics Engineering) took important missions in the software testing phases of the study in detail. We thank him for the achievement in this study.

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Authors and Affiliations

  1. Department of Electrical Electronics Engineering/Biomedical, Gazi University, Ankara, Turkey

    Berkan Ural & Fırat Hardalaç

  2. Faculty of Medicine/Neurosurgery, TOBB ETU, Ankara, Turkey

    Pınar Özışık

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  1. Berkan Ural
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Contributions

Conceptualization: Berkan URAL. Design of study: Berkan URAL. Analyzing data: Pınar AKDEMİR ÖZIŞIK, Berkan URAL. Methodology: Berkan URAL. (Main) Software: Berkan URAL. Validation & Test: Berkan URAL. Software development: Berkan URAL. Writing – original draft: Berkan URAL. Writing – review & editing: Berkan URAL, Pınar AKDEMİR ÖZIŞIK, Fırat HARDALAÇ.

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Correspondence to Berkan Ural.

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Ethical Approval

This study is mainly classified as a retrospective study. All procedures performed in the study involving radiological images of human subjects and were in accordance with the ethical standards of the institutional and/or national research comittee and with the latest Helsinki declaration.

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Ural, B., Özışık, P. & Hardalaç, F. An improved computer based diagnosis system for early detection of abnormal lesions in the brain tissues with using magnetic resonance and computerized tomography images. Multimed Tools Appl 79, 15613–15634 (2020). https://doi.org/10.1007/s11042-019-07823-7

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