PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor Detection
Brain Tumor holds a significant holds in human health, classified into three primary types: glioma, meningioma, and pituitary tumors. Early detection and accurate classification are vital for effective diagnosis and lowering healthcare costs. In PBvit we presents a novel brain tumor detection framew...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10811909/ |
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| author | Pratikkumar Chauhan Munindra Lunagaria Deepak Kumar Verma Krunal Vaghela Ghanshyam G. Tejani Sunil Kumar Sharma Ahmad Raza Khan |
| author_facet | Pratikkumar Chauhan Munindra Lunagaria Deepak Kumar Verma Krunal Vaghela Ghanshyam G. Tejani Sunil Kumar Sharma Ahmad Raza Khan |
| author_sort | Pratikkumar Chauhan |
| collection | DOAJ |
| description | Brain Tumor holds a significant holds in human health, classified into three primary types: glioma, meningioma, and pituitary tumors. Early detection and accurate classification are vital for effective diagnosis and lowering healthcare costs. In PBvit we presents a novel brain tumor detection framework, the Patch Base Vision Transformer (PBVit). PBVit adopts a patch-based approach where input tumor images are divided into fixed-size patches, with each patch treated as a token. These image patches are linearly projected into lower-dimensional token embeddings, and positional encodings are added to help the model understand spatial relationships within the image. PBVit enhances the detection of intricate patterns and anomalies in brain scans, improving diagnostic accuracy. We trained PBVit using the Figshare brain tumor dataset and observed notable performance improvements compared to traditional CNN-based models. The PBVit reached an accuracy of 95.8%, a precision of 95.3%, a recall of 93.2%, and an F1-score of 92%, indicating its robustness in identifying brain tumors. The promising results demonstrate that PBVit can play a important role in facilitating early-stage diagnosis, reducing unnecessary biopsies, and ultimately enhancing patient care, while also showcasing the potential of transformer-based architectures in medical imaging. |
| format | Article |
| id | doaj-art-a088a2859e34443c93be725facc2917d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a088a2859e34443c93be725facc2917d2025-01-25T00:00:41ZengIEEEIEEE Access2169-35362025-01-0113130151302910.1109/ACCESS.2024.352100210811909PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor DetectionPratikkumar Chauhan0https://orcid.org/0009-0009-2034-1682Munindra Lunagaria1https://orcid.org/0009-0009-4667-4231Deepak Kumar Verma2https://orcid.org/0000-0002-4745-1752Krunal Vaghela3https://orcid.org/0000-0001-9847-4754Ghanshyam G. Tejani4https://orcid.org/0000-0001-9106-0313Sunil Kumar Sharma5https://orcid.org/0000-0002-1732-2677Ahmad Raza Khan6https://orcid.org/0000-0002-5365-9189Department of Computer Engineering, Marwadi University, Rajkot, Gujarat, IndiaDepartment of Computer Engineering, Marwadi University, Rajkot, Gujarat, IndiaDepartment of Computer Engineering, Marwadi University, Rajkot, Gujarat, IndiaDepartment of Computer Engineering, Marwadi University, Rajkot, Gujarat, IndiaDepartment of Industrial Engineering and Management, Yuan Ze University, Taoyuan, TaiwanDepartment of Information System, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi ArabiaBrain Tumor holds a significant holds in human health, classified into three primary types: glioma, meningioma, and pituitary tumors. Early detection and accurate classification are vital for effective diagnosis and lowering healthcare costs. In PBvit we presents a novel brain tumor detection framework, the Patch Base Vision Transformer (PBVit). PBVit adopts a patch-based approach where input tumor images are divided into fixed-size patches, with each patch treated as a token. These image patches are linearly projected into lower-dimensional token embeddings, and positional encodings are added to help the model understand spatial relationships within the image. PBVit enhances the detection of intricate patterns and anomalies in brain scans, improving diagnostic accuracy. We trained PBVit using the Figshare brain tumor dataset and observed notable performance improvements compared to traditional CNN-based models. The PBVit reached an accuracy of 95.8%, a precision of 95.3%, a recall of 93.2%, and an F1-score of 92%, indicating its robustness in identifying brain tumors. The promising results demonstrate that PBVit can play a important role in facilitating early-stage diagnosis, reducing unnecessary biopsies, and ultimately enhancing patient care, while also showcasing the potential of transformer-based architectures in medical imaging.https://ieeexplore.ieee.org/document/10811909/Brain tumor detectionvision transformerhealthcare brain tumor detectionCNN PBvitDETIR |
| spellingShingle | Pratikkumar Chauhan Munindra Lunagaria Deepak Kumar Verma Krunal Vaghela Ghanshyam G. Tejani Sunil Kumar Sharma Ahmad Raza Khan PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor Detection IEEE Access Brain tumor detection vision transformer healthcare brain tumor detection CNN PBvit DETIR |
| title | PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor Detection |
| title_full | PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor Detection |
| title_fullStr | PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor Detection |
| title_full_unstemmed | PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor Detection |
| title_short | PBVit: A Patch-Based Vision Transformer for Enhanced Brain Tumor Detection |
| title_sort | pbvit a patch based vision transformer for enhanced brain tumor detection |
| topic | Brain tumor detection vision transformer healthcare brain tumor detection CNN PBvit DETIR |
| url | https://ieeexplore.ieee.org/document/10811909/ |
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