Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent Unit
Generally, traumatic and aneurysmal brain injuries cause intracranial hemorrhages, which is a severe disease that results in death, if it is not treated and diagnosed properly at the early stage. Compared to other imaging techniques, Computed Tomography (CT) images are extensively utilized by clinic...
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/10851 |
| _version_ | 1797469305562988544 |
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| author | Jewel Sengupta Robertas Alzbutas |
| author_facet | Jewel Sengupta Robertas Alzbutas |
| author_sort | Jewel Sengupta |
| collection | DOAJ |
| description | Generally, traumatic and aneurysmal brain injuries cause intracranial hemorrhages, which is a severe disease that results in death, if it is not treated and diagnosed properly at the early stage. Compared to other imaging techniques, Computed Tomography (CT) images are extensively utilized by clinicians for locating and identifying intracranial hemorrhage regions. However, it is a time-consuming and complex task, which majorly depends on professional clinicians. To highlight this problem, a novel model is developed for the automatic detection of intracranial hemorrhages. After collecting the 3D CT scans from the Radiological Society of North America (RSNA) 2019 brain CT hemorrhage database, the image segmentation is carried out using Fuzzy C Means (FCM) clustering algorithm. Then, the hybrid feature extraction is accomplished on the segmented regions utilizing the Histogram of Oriented Gradients (HoG), Local Ternary Pattern (LTP), and Local Binary Pattern (LBP) to extract discriminative features. Furthermore, the Cuckoo Search Optimization (CSO) algorithm and the Optimized Gated Recurrent Unit (OGRU) classifier are integrated for feature selection and sub-type classification of intracranial hemorrhages. In the resulting segment, the proposed ORGU-CSO model obtained 99.36% of classification accuracy, which is higher related to other considered classifiers. |
| first_indexed | 2024-03-09T19:18:35Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:18:35Z |
| publishDate | 2022-10-01 |
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| spelling | doaj.art-e83c8cfac7f74642844f947fed1ffe3e2023-11-24T03:33:50ZengMDPI AGApplied Sciences2076-34172022-10-0112211085110.3390/app122110851Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent UnitJewel Sengupta0Robertas Alzbutas1Department of Applied Mathematics, Kaunas University of Technology, K. Donelaičio g. 73, 44249 Kaunas, LithuaniaDepartment of Applied Mathematics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, K. Donelaičio g. 73, 44249 Kaunas, LithuaniaGenerally, traumatic and aneurysmal brain injuries cause intracranial hemorrhages, which is a severe disease that results in death, if it is not treated and diagnosed properly at the early stage. Compared to other imaging techniques, Computed Tomography (CT) images are extensively utilized by clinicians for locating and identifying intracranial hemorrhage regions. However, it is a time-consuming and complex task, which majorly depends on professional clinicians. To highlight this problem, a novel model is developed for the automatic detection of intracranial hemorrhages. After collecting the 3D CT scans from the Radiological Society of North America (RSNA) 2019 brain CT hemorrhage database, the image segmentation is carried out using Fuzzy C Means (FCM) clustering algorithm. Then, the hybrid feature extraction is accomplished on the segmented regions utilizing the Histogram of Oriented Gradients (HoG), Local Ternary Pattern (LTP), and Local Binary Pattern (LBP) to extract discriminative features. Furthermore, the Cuckoo Search Optimization (CSO) algorithm and the Optimized Gated Recurrent Unit (OGRU) classifier are integrated for feature selection and sub-type classification of intracranial hemorrhages. In the resulting segment, the proposed ORGU-CSO model obtained 99.36% of classification accuracy, which is higher related to other considered classifiers.https://www.mdpi.com/2076-3417/12/21/10851cuckoo search optimizerFuzzy C Meangated recurrent unithybrid feature extractionintracranial hemorrhage |
| spellingShingle | Jewel Sengupta Robertas Alzbutas Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent Unit Applied Sciences cuckoo search optimizer Fuzzy C Mean gated recurrent unit hybrid feature extraction intracranial hemorrhage |
| title | Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent Unit |
| title_full | Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent Unit |
| title_fullStr | Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent Unit |
| title_full_unstemmed | Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent Unit |
| title_short | Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent Unit |
| title_sort | intracranial hemorrhages segmentation and features selection applying cuckoo search algorithm with gated recurrent unit |
| topic | cuckoo search optimizer Fuzzy C Mean gated recurrent unit hybrid feature extraction intracranial hemorrhage |
| url | https://www.mdpi.com/2076-3417/12/21/10851 |
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