Combining deep and handcrafted image features for MRI brain scan classification

Hasan, A, Jalab, HA, Meziane, F ORCID: https://orcid.org/0000-0001-9811-6914, Kahtan, H and Al-Ahmad, AS 2019, 'Combining deep and handcrafted image features for MRI brain scan classification' , IEEE Access, 7 (1) , pp. 79959-79967.

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Abstract

Progresses in the areas of artificial intelligence, machine learning, and medical imaging technologies have allowed the development of the medical image processing field with some astonishing results in the last two decades. These innovations enabled the clinicians to view the human body in high-resolution or three-dimensional cross-sectional slices, which resulted in an increase in the accuracy of the diagnosis and the examination of patients in a non-invasive manner. The fundamental step for MRI brain scans classifiers is their ability to extract meaningful features. As a result, many works have proposed different methods for features extraction to classify the abnormal growths in brain MRI scans. More recently, the application of deep learning algorithms to medical imaging lead to impressive performance enhancements in classifying and diagnosing complicated pathologies such as brain tumors. In this study, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. In parallel, handcrafted features are extracted using the modified grey level co-occurrence matrix (MGLCM) method. Subsequently, the extracted relevant features are combined with handcrafted features to improve the classification process of MRI brain scans with SVM used as the classifier. The obtained results proved that the combination of the deep learning approach and the handcrafted features extracted by MGLCM improves the accuracy of classification of the SVM classifier up to 99.30%.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: IEEE Access
Publisher: IEEE
ISSN: 2169-3536
Related URLs:
Depositing User: Prof Farid Meziane
Date Deposited: 17 Jun 2019 08:42
Last Modified: 15 Nov 2019 10:59
URI: http://usir.salford.ac.uk/id/eprint/51567

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