Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices
The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/1/62 |
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| author | Akmalbek Abdusalomov Sanjar Mirzakhalilov Sabina Umirzakova Abror Shavkatovich Buriboev Azizjon Meliboev Bahodir Muminov Heung Seok Jeon |
| author_facet | Akmalbek Abdusalomov Sanjar Mirzakhalilov Sabina Umirzakova Abror Shavkatovich Buriboev Azizjon Meliboev Bahodir Muminov Heung Seok Jeon |
| author_sort | Akmalbek Abdusalomov |
| collection | DOAJ |
| description | The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions. The modified RetinaNet achieves an average precision (AP) of 32.1, surpassing state-of-the-art models in small tumor detection (AP<sub>S</sub>: 14.3) and large tumor localization (AP<sub>L</sub>: 49.7). Furthermore, the model significantly reduces computational costs, making real-time analysis feasible on low-power hardware. Clinical relevance is a key focus of this work. The proposed model addresses the diagnostic challenges of small, variable-sized tumors often overlooked by existing methods. Its lightweight architecture enables accurate and timely tumor localization on portable devices, bridging the gap in diagnostic accessibility for underserved regions. Extensive experiments on the BRATS dataset demonstrate the model robustness across tumor sizes and configurations, with confidence scores consistently exceeding 81%. This advancement holds the potential for improving early tumor detection, particularly in remote areas lacking advanced medical infrastructure, thereby contributing to better patient outcomes and broader accessibility to AI-driven diagnostic tools. |
| format | Article |
| id | doaj-art-d39576e59c57472eaa353d269d38dfa8 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-d39576e59c57472eaa353d269d38dfa82025-01-24T13:23:08ZengMDPI AGBioengineering2306-53542025-01-011216210.3390/bioengineering12010062Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge DevicesAkmalbek Abdusalomov0Sanjar Mirzakhalilov1Sabina Umirzakova2Abror Shavkatovich Buriboev3Azizjon Meliboev4Bahodir Muminov5Heung Seok Jeon6Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Republic of KoreaDepartment of Computer Systems, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent 100200, UzbekistanDepartment of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Republic of KoreaDepartment of AI-Software, Gachon University, Seongnam-si 13120, Republic of KoreaDepartment of Digital Technologies and Mathematics, Kokand University, Fergana 150700, UzbekistanDepartment of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, UzbekistanDepartment of Software Technology, Konkuk University, Chungju 27478, Republic of KoreaThe timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions. The modified RetinaNet achieves an average precision (AP) of 32.1, surpassing state-of-the-art models in small tumor detection (AP<sub>S</sub>: 14.3) and large tumor localization (AP<sub>L</sub>: 49.7). Furthermore, the model significantly reduces computational costs, making real-time analysis feasible on low-power hardware. Clinical relevance is a key focus of this work. The proposed model addresses the diagnostic challenges of small, variable-sized tumors often overlooked by existing methods. Its lightweight architecture enables accurate and timely tumor localization on portable devices, bridging the gap in diagnostic accessibility for underserved regions. Extensive experiments on the BRATS dataset demonstrate the model robustness across tumor sizes and configurations, with confidence scores consistently exceeding 81%. This advancement holds the potential for improving early tumor detection, particularly in remote areas lacking advanced medical infrastructure, thereby contributing to better patient outcomes and broader accessibility to AI-driven diagnostic tools.https://www.mdpi.com/2306-5354/12/1/62brain tumor detectionmedical edge deviceslightweight modelmedical image analysislow-resource settings |
| spellingShingle | Akmalbek Abdusalomov Sanjar Mirzakhalilov Sabina Umirzakova Abror Shavkatovich Buriboev Azizjon Meliboev Bahodir Muminov Heung Seok Jeon Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices Bioengineering brain tumor detection medical edge devices lightweight model medical image analysis low-resource settings |
| title | Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices |
| title_full | Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices |
| title_fullStr | Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices |
| title_full_unstemmed | Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices |
| title_short | Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices |
| title_sort | accessible ai diagnostics and lightweight brain tumor detection on medical edge devices |
| topic | brain tumor detection medical edge devices lightweight model medical image analysis low-resource settings |
| url | https://www.mdpi.com/2306-5354/12/1/62 |
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