Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model
Brain tumors can be serious; consequently, rapid and accurate detection is crucial. Nevertheless, a variety of obstacles, such as poor imaging resolution, doubts over the accuracy of data, a lack of diverse tumor classes and stages, and the possibility of misunderstanding, present challenges to achi...
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MDPI AG
2024-11-01
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| author | Abeer Fayez Al Bataineh Khalid M. O. Nahar Hayel Khafajeh Ghassan Samara Raed Alazaidah Ahmad Nasayreh Ayah Bashkami Hasan Gharaibeh Waed Dawaghreh |
| author_facet | Abeer Fayez Al Bataineh Khalid M. O. Nahar Hayel Khafajeh Ghassan Samara Raed Alazaidah Ahmad Nasayreh Ayah Bashkami Hasan Gharaibeh Waed Dawaghreh |
| author_sort | Abeer Fayez Al Bataineh |
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| description | Brain tumors can be serious; consequently, rapid and accurate detection is crucial. Nevertheless, a variety of obstacles, such as poor imaging resolution, doubts over the accuracy of data, a lack of diverse tumor classes and stages, and the possibility of misunderstanding, present challenges to achieve an accurate and final diagnosis. Effective brain cancer detection is crucial for patients’ safety and health. Deep learning systems provide the capability to assist radiologists in quickly and accurately detecting diagnoses. This study presents an innovative deep learning approach that utilizes the Swin Transformer. The suggested method entails integrating the Swin Transformer with the pretrained deep learning model Resnet50V2, called (SwT+Resnet50V2). The objective of this modification is to decrease memory utilization, enhance classification accuracy, and reduce training complexity. The self-attention mechanism of the Swin Transformer identifies distant relationships and captures the overall context. Resnet 50V2 improves both accuracy and training speed by extracting adaptive features from the Swin Transformer’s dependencies. We evaluate the proposed framework using two publicly accessible brain magnetic resonance imaging (MRI) datasets, each including two and four distinct classes, respectively. Employing data augmentation and transfer learning techniques enhances model performance, leading to more dependable and cost-effective training. The suggested model achieves an impressive accuracy of 99.9% on the binary-labeled dataset and 96.8% on the four-labeled dataset, outperforming the VGG16, MobileNetV2, Resnet50V2, EfficientNetV2B3, ConvNeXtTiny, and convolutional neural network (CNN) algorithms used for comparison. This demonstrates that the Swin transducer, when combined with Resnet50V2, is capable of accurately diagnosing brain tumors. This method leverages the combination of SwT+Resnet50V2 to create an innovative diagnostic tool. Radiologists have the potential to accelerate and improve the detection of brain tumors, leading to improved patient outcomes and reduced risks. |
| format | Article |
| id | doaj-art-bf26d6fe5d104f0290a9683aa19f6505 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-bf26d6fe5d104f0290a9683aa19f65052025-08-20T02:26:54ZengMDPI AGApplied Sciences2076-34172024-11-0114221015410.3390/app142210154Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 ModelAbeer Fayez Al Bataineh0Khalid M. O. Nahar1Hayel Khafajeh2Ghassan Samara3Raed Alazaidah4Ahmad Nasayreh5Ayah Bashkami6Hasan Gharaibeh7Waed Dawaghreh8Department of Scientific Services Courses, Yarmouk University, Irbid 211633, JordanFaculty of Computer Studies, Arab Open University, P.O. Box 84901, Riyadh 11681, Saudi ArabiaDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, JordanDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, JordanDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, JordanDepartment of Information Technology and Computer Sciences, Yarmouk University, Irbid 211633, JordanDepartment of Medical Laboratory Sciences, Al Balqa Applied University, Salt 11134, JordanDepartment of Information Technology and Computer Sciences, Yarmouk University, Irbid 211633, JordanDepartment of Information Technology and Computer Sciences, Yarmouk University, Irbid 211633, JordanBrain tumors can be serious; consequently, rapid and accurate detection is crucial. Nevertheless, a variety of obstacles, such as poor imaging resolution, doubts over the accuracy of data, a lack of diverse tumor classes and stages, and the possibility of misunderstanding, present challenges to achieve an accurate and final diagnosis. Effective brain cancer detection is crucial for patients’ safety and health. Deep learning systems provide the capability to assist radiologists in quickly and accurately detecting diagnoses. This study presents an innovative deep learning approach that utilizes the Swin Transformer. The suggested method entails integrating the Swin Transformer with the pretrained deep learning model Resnet50V2, called (SwT+Resnet50V2). The objective of this modification is to decrease memory utilization, enhance classification accuracy, and reduce training complexity. The self-attention mechanism of the Swin Transformer identifies distant relationships and captures the overall context. Resnet 50V2 improves both accuracy and training speed by extracting adaptive features from the Swin Transformer’s dependencies. We evaluate the proposed framework using two publicly accessible brain magnetic resonance imaging (MRI) datasets, each including two and four distinct classes, respectively. Employing data augmentation and transfer learning techniques enhances model performance, leading to more dependable and cost-effective training. The suggested model achieves an impressive accuracy of 99.9% on the binary-labeled dataset and 96.8% on the four-labeled dataset, outperforming the VGG16, MobileNetV2, Resnet50V2, EfficientNetV2B3, ConvNeXtTiny, and convolutional neural network (CNN) algorithms used for comparison. This demonstrates that the Swin transducer, when combined with Resnet50V2, is capable of accurately diagnosing brain tumors. This method leverages the combination of SwT+Resnet50V2 to create an innovative diagnostic tool. Radiologists have the potential to accelerate and improve the detection of brain tumors, leading to improved patient outcomes and reduced risks.https://www.mdpi.com/2076-3417/14/22/10154Swin Transformerbrain tumor classificationdeep learningvision transformerMRI image |
| spellingShingle | Abeer Fayez Al Bataineh Khalid M. O. Nahar Hayel Khafajeh Ghassan Samara Raed Alazaidah Ahmad Nasayreh Ayah Bashkami Hasan Gharaibeh Waed Dawaghreh Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model Applied Sciences Swin Transformer brain tumor classification deep learning vision transformer MRI image |
| title | Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model |
| title_full | Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model |
| title_fullStr | Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model |
| title_full_unstemmed | Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model |
| title_short | Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model |
| title_sort | enhanced magnetic resonance imaging based brain tumor classification with a hybrid swin transformer and resnet50v2 model |
| topic | Swin Transformer brain tumor classification deep learning vision transformer MRI image |
| url | https://www.mdpi.com/2076-3417/14/22/10154 |
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