Classification Model of Breast Masses in DCE-MRI Using Kinetic Curves Features with Quantum-Raina’s Polynomial Based Fusion

Journal article

Hasan, A.M., Al-Waely, N.K.N., Ajobouri, H.K., Ibrahim, R.W., Jalab, H.A. and Meziane, F. 2023. Classification Model of Breast Masses in DCE-MRI Using Kinetic Curves Features with Quantum-Raina’s Polynomial Based Fusion. Biomedical Signal Processing and Control. 84, pp. 1-12. https://doi.org/10.1016/j.bspc.2023.105002
AuthorsHasan, A.M., Al-Waely, N.K.N., Ajobouri, H.K., Ibrahim, R.W., Jalab, H.A. and Meziane, F.
Abstract

Breast cancer is the most common cancer among women worldwide and is currently the second leading cause of cancer death in women. However, early detection and diagnosis of breast cancer can lead to a complete remission and can extend survival periods. Dynamic Contrast Enhanced (DCE-MRI) is still being used in diagnosis, particularly for breast cancer. Although manual examination by medical professionals is thought to be the main diagnostic method for breast cancer, it has many drawbacks. The most important problem is that earlier research did not consider breast cancer characteristics features that might be useful for more accurately identifying breast tumor malignancy. The use of recently developed image feature extraction algorithms in conjunction with radiological imaging aids in the diagnosis and categorization of breast cancer. With this view, the current study presents a novel fusion model using the kinetic curves features with a proposed quantum-Raina’s polynomial features for the breast cancer classification in MRI. This study uses two approaches for features extractions of DCE-MRI scans of the breast, namely, kinetic and QRP features. The excellent performance of the fusion extracted features improves the classification accuracy of breast into benign and malignant lesions. The maximum achieved accuracy for classifying a dataset comprising 300 DCE-MRI breast scans is 97.4%. The accuracy of the proposed classification model is substantial with a low complexity rate.

KeywordsBreast Masses; DCE-MRI; Kinetic Curves; Quantum-Raina’s Polynomial
Year2023
JournalBiomedical Signal Processing and Control
Journal citation84, pp. 1-12
PublisherElseiver
ISSN1746-8108
Digital Object Identifier (DOI)https://doi.org/10.1016/j.bspc.2023.105002
Web address (URL)https://www.sciencedirect.com/science/article/pii/S1746809423004354
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Output statusPublished
Publication dates
Online11 May 2023
Publication process dates
Accepted30 Apr 2023
Deposited02 Jun 2023
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