Laçi Hafsa, Sevrani Kozeta, Iqbal Sarfraz
Department of Statistics and Applied Informatics, Faculty of Economy, University of Tirana, Tirana, Albania.
Department of Informatics, Faculty of Technology, Linnaeus University, Växjö, Sweden.
BMC Med Imaging. 2025 May 7;25(1):156. doi: 10.1186/s12880-025-01701-5.
Medical images occupy the largest part of the existing medical information and dealing with them is challenging not only in terms of management but also in terms of interpretation and analysis. Hence, analyzing, understanding, and classifying them, becomes a very expensive and time-consuming task, especially if performed manually. Deep learning is considered a good solution for image classification, segmentation, and transfer learning tasks since it offers a large number of algorithms to solve such complex problems. PRISMA-ScR guidelines have been followed to conduct the scoping review with the aim of exploring how deep learning is being used to classify a broad spectrum of diseases diagnosed using an X-ray, MRI, or Ultrasound image modality.Findings contribute to the existing research by outlining the characteristics of the adopted datasets and the preprocessing or augmentation techniques applied to them. The authors summarized all relevant studies based on the deep learning models used and the accuracy achieved for classification. Whenever possible, they included details about the hardware and software configurations, as well as the architectural components of the models employed. Moreover, the models that achieved the highest accuracy in disease classification were highlighted, along with their strengths. The authors also discussed the limitations of the current approaches and proposed future directions for medical image classification.
医学图像占据了现有医学信息的最大部分，处理这些图像不仅在管理方面具有挑战性，在解读和分析方面也颇具难度。因此，对它们进行分析、理解和分类成为一项成本高昂且耗时的任务，尤其是手动进行时。深度学习被认为是图像分类、分割和迁移学习任务的良好解决方案，因为它提供了大量算法来解决此类复杂问题。本研究遵循PRISMA-ScR指南进行范围综述，旨在探索深度学习如何用于对通过X射线、磁共振成像（MRI）或超声图像模态诊断出的广泛疾病进行分类。研究结果通过概述所采用数据集的特征以及应用于这些数据集的预处理或增强技术，为现有研究做出了贡献。作者基于所使用的深度学习模型和分类所达到的准确率总结了所有相关研究。只要有可能，他们就纳入了有关硬件和软件配置以及所采用模型的架构组件的详细信息。此外，还突出了在疾病分类中实现最高准确率的模型及其优势。作者还讨论了当前方法的局限性，并提出了医学图像分类的未来方向。
