Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, QC, H3T1J4, Canada.
Department of Computer Science, Jordan University of Science and Technology, Ar-Ramtha, Jordan.
Sci Rep. 2024 Apr 5;14(1):8071. doi: 10.1038/s41598-024-58283-5.
Over recent years, researchers and practitioners have encountered massive and continuous improvements in the computational resources available for their use. This allowed the use of resource-hungry Machine learning (ML) algorithms to become feasible and practical. Moreover, several advanced techniques are being used to boost the performance of such algorithms even further, which include various transfer learning techniques, data augmentation, and feature concatenation. Normally, the use of these advanced techniques highly depends on the size and nature of the dataset being used. In the case of fine-grained medical image sets, which have subcategories within the main categories in the image set, there is a need to find the combination of the techniques that work the best on these types of images. In this work, we utilize these advanced techniques to find the best combinations to build a state-of-the-art lumber disc herniation computer-aided diagnosis system. We have evaluated the system extensively and the results show that the diagnosis system achieves an accuracy of 98% when it is compared with human diagnosis.
近年来，研究人员和从业者在可用于其使用的计算资源方面取得了巨大而持续的进步。这使得资源密集型机器学习 (ML) 算法的使用变得可行和实用。此外，还使用了几种高级技术来进一步提高这些算法的性能，其中包括各种迁移学习技术、数据增强和特征连接。通常，这些高级技术的使用高度取决于正在使用的数据集的大小和性质。在精细的医学图像集中，图像集中的主类别中有子类别，因此需要找到最适合这些类型图像的技术组合。在这项工作中，我们利用这些高级技术来找到最佳组合，构建最先进的腰椎间盘突出症计算机辅助诊断系统。我们对该系统进行了广泛评估，结果表明，与人工诊断相比，该诊断系统的准确率达到 98%。
