Real-time medical lesion screening: accurate and rapid detectors

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Real-Time Image Processing Pub Date : 2024-07-17 DOI:10.1007/s11554-024-01512-x

Dangguo Shao, Jie Jiang, Lei Ma, Hua Lai, Sanli Yi

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Abstract

Brain tumors are highly lethal, representing 85–90% of all primary central nervous system (CNS) tumors. Magnetic resonance imaging (MRI) images are employed to identify and assess brain tumors. However, this process has historically relied heavily on the expertise of medical professionals and necessitated the involvement of a substantial number of personnel. To optimize the allocation of medical resources and improve diagnostic efficiency, this work proposes a DETR-based RPC–DETR model that utilizes the Transformer. We conducted comparative experiments using RPC–DETR with other traditional detectors on the same equipment to test the performance exhibited by the model with equal computational resources. Tested on the Br35H brain tumor dataset with 701 MRI images (500 training sets and 201 test sets), RPC–DETR surpasses the YOLO models in accuracy while utilizing fewer parameters. This advancement ensures more reliable and faster diagnosis. RPC–DETR achieves a 96% mAP with just 14M parameters, offering high accuracy in brain tumor detection with a lighter model, making it easier to implement in various medical settings.

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实时医疗病灶筛查：准确快速的检测器

脑肿瘤致死率很高，占所有原发性中枢神经系统（CNS）肿瘤的 85-90%。磁共振成像（MRI）图像可用于识别和评估脑肿瘤。然而，这一过程历来严重依赖医疗专业人员的专业知识，需要大量人员的参与。为了优化医疗资源配置，提高诊断效率，本研究提出了一种基于 DETR 的 RPC-DETR 模型，该模型利用了 Transformer。我们使用 RPC-DETR 与相同设备上的其他传统检测器进行了对比实验，以测试该模型在同等计算资源下的性能表现。RPC-DETR 在 Br35H 脑肿瘤数据集（包含 701 幅核磁共振图像（500 个训练集和 201 个测试集））上进行了测试，其准确性超过了 YOLO 模型，同时使用的参数更少。这一进步确保了更可靠、更快速的诊断。RPC-DETR 仅用 1400 万个参数就达到了 96% 的 mAP，以更轻的模型提供了脑肿瘤检测的高准确性，使其更易于在各种医疗环境中实施。

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来源期刊

Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

6.80

自引率

6.70%

发文量

审稿时长

6 months

期刊介绍： Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.