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PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI

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Abstract

Detecting neurological abnormalities such as brain tumors and Alzheimer’s disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.

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Data Availability

The data used in this study were downloaded from https://www.kaggle.com/tourist55/alzheimers-dataset-4-class-of-images, https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri, and https://wiki.cancerimagingarchive.net/.

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Authors and Affiliations

  1. Department of Radiology, Elazig Fethi Sekin City Hospital, Elazig, Turkey

    Ela Kaplan

  2. Imaging Department, Gleneagles Hospital Kuala Lumpur, Jalan Ampang, Kampung Berembang, 50450, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia

    Wai Yee Chan

  3. Department of Radiology, Faculty of Medicine, Duzce University, Duzce, Turkey

    Hasan Baki Altinsoy

  4. Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum, Turkey

    Mehmet Baygin

  5. School of Business (Information System), University of Southern Queensland, Springfield, Australia

    Prabal Datta Barua

  6. Faculty of Science, Agriculture, Business and Law, School of Science and Technology, University of New England, Armidale, NSW, 2351, Australia

    Subrata Chakraborty

  7. Center for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Subrata Chakraborty

  8. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

    Sengul Dogan & Turker Tuncer

  9. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    U. Rajendra Acharya

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Contributions

Conceptualization, EK, WYC, HBA, MB, PDB, SC, SD, TT, URA; formal analysis, EK, WYC, HBA, MB, PDB; investigation, EK, WYC, HBA, MB, PDB, SC, SD, TT, URA; methodology, EK, WYC, HBA, MB, PDB; software, SD, TT; project administration, URA; resources, EK, WYC, HBA; supervision, URA; validation, EK, WYC, HBA, MB, PDB, SC, SD, TT, URA; visualization, EK, WYC, HBA, MB, PDB, SC, SD, TT, URA; writing—original draft, EK, WYC, HBA, MB, PDB, SC, SD, TT, URA; writing—review and editing, EK, WYC, HBA, MB, PDB, SC, SD, TT, URA. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Sengul Dogan.

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Kaplan, E., Chan, W.Y., Altinsoy, H.B. et al. PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI. J Digit Imaging 36, 2441–2460 (2023). https://doi.org/10.1007/s10278-023-00889-8

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