PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI
Article
Kaplan, Ela, Chan, Wai Yee, Altinsoy, Hasan Baki, Baygin, Mehmet, Barua, Prabal Datta, Chakraborty, Subrata, Dogan, Sengul, Tuncer, Turker and Acharya, U. Rajendra. 2023. "PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI." Journal of Digital Imaging. 23, p. 2441–2460. https://doi.org/10.1007/s10278-023-00889-8
| Article Title | PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI |
|---|---|
| ERA Journal ID | 35211 |
| Article Category | Article |
| Authors | Kaplan, Ela, Chan, Wai Yee, Altinsoy, Hasan Baki, Baygin, Mehmet, Barua, Prabal Datta, Chakraborty, Subrata, Dogan, Sengul, Tuncer, Turker and Acharya, U. Rajendra |
| Journal Title | Journal of Digital Imaging |
| Journal Citation | 23, p. 2441–2460 |
| Number of Pages | 20 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 0897-1889 |
| 1618-727X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s10278-023-00889-8 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s10278-023-00889-8 |
| 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. |
| Keywords | Biomedical image processing; Brain MRI; Pyramid and fixed-size patch feature extraction; HOG; Computer vision |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Elazig Fethi Sekin City Hospital, Turkey |
| Gleneagles Hospital, Malaysia | |
| Duzce University, Turkey | |
| Erzurum Technical University, Turkey | |
| School of Business | |
| University of New England | |
| University of Technology Sydney | |
| Firat University, Turkey | |
| School of Mathematics, Physics and Computing |
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