Abdusalomov Akmalbek, Mirzakhalilov Sanjar, Umirzakova Sabina, Shavkatovich Buriboev Abror, Meliboev Azizjon, Muminov Bahodir, Jeon Heung Seok
Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Republic of Korea.
Department of Computer Systems, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan.
Bioengineering (Basel). 2025 Jan 13;12(1):62. doi: 10.3390/bioengineering12010062.
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: 14.3) and large tumor localization (AP: 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.
及时准确地检测脑肿瘤对于有效的医疗干预至关重要，尤其是在资源有限的环境中。本研究提出了一种专为医疗边缘设备部署量身定制的轻量级高效RetinaNet变体。该模型通过用MobileNet取代计算密集型的ResNet主干并利用深度可分离卷积，在保持高检测精度的同时减少了计算开销。改进后的RetinaNet平均精度（AP）达到32.1，在小肿瘤检测（AP：14.3）和大肿瘤定位（AP：49.7）方面超过了现有模型。此外，该模型显著降低了计算成本，使得在低功耗硬件上进行实时分析成为可能。临床相关性是这项工作的关键重点。所提出的模型解决了现有方法经常忽略的小尺寸、大小不一的肿瘤的诊断挑战。其轻量级架构能够在便携式设备上准确及时地进行肿瘤定位，弥合了服务不足地区在诊断可及性方面的差距。在BRATS数据集上进行的大量实验证明了该模型在不同肿瘤大小和配置下的鲁棒性，置信度分数始终超过81%。这一进展有可能改善早期肿瘤检测，特别是在缺乏先进医疗基础设施的偏远地区，从而有助于改善患者预后并更广泛地普及人工智能驱动的诊断工具。
