Abstract
Medical image classification plays an increasingly vital role in identifying various diseases by classifying medical images, such as X-rays, MRIs and CT scans, into different categories based on their features. In recent years, deep learning techniques have attracted significant attention in medical image classification. However, it is usually infeasible to train an entire large deep learning model from scratch. To address this issue, one of the solutions is the transfer learning (TL) technique, where a pre-trained model is reused for a new task. In this paper, we present a comprehensive analysis of TL techniques for medical image classification using deep convolutional neural networks. We evaluate six pre-trained models (AlexNet, VGG16, ResNet18, ResNet34, ResNet50, and InceptionV3) on a custom chest X-ray dataset for disease detection. The experimental results demonstrate that InceptionV3 consistently outperforms other models across all the standard metrics. The ResNet family shows progressively better performance with increasing depth, whereas VGG16 and AlexNet perform reasonably well but with lower accuracy. In addition, we also conduct uncertainty analysis and runtime comparison to assess the robustness and computational efficiency of these models. Our findings reveal that TL is beneficial in most cases, especially with limited data, but the extent of improvement depends on several factors such as model architecture, dataset size, and domain similarity between source and target tasks. Moreover, we demonstrate that with a well-trained feature extractor, only a lightweight feedforward model is enough to provide efficient prediction. As such, this study contributes to the understanding of TL in medical image classification, and provides insights for selecting appropriate models based on specific requirements.
Abstract (translated)
医学图像分类在通过将X光、MRI和CT扫描等医疗影像根据其特征归类到不同的类别来识别各种疾病方面扮演着越来越重要的角色。近年来，深度学习技术在医学图像分类中引起了广泛关注。然而，通常从头开始训练整个大型深度学习模型是不切实际的。为了解决这个问题，解决方案之一就是迁移学习（Transfer Learning, TL）技术，在该技术中预训练好的模型被复用于新的任务上。本文我们对基于深度卷积神经网络的医学图像分类中的迁移学习方法进行了全面分析，并在自定义胸部X光数据集上评估了六种预训练模型（AlexNet、VGG16、ResNet18、ResNet34、ResNet50和InceptionV3）用于疾病检测的效果。实验结果表明，InceptionV3在整个标准评价指标中始终优于其他模型。随着深度的增加，ResNet家族的表现逐渐变好；而VGG16和AlexNet表现得相当不错但准确性较低。此外，我们还进行了不确定性分析和运行时间比较，以评估这些模型的鲁棒性和计算效率。我们的发现表明，在大多数情况下迁移学习是有益的，尤其是在数据有限的情况下，但是改进的程度依赖于诸如模型架构、数据集大小以及源任务与目标任务之间的领域相似性等若干因素。此外，我们还证明了即使只有轻量级前馈模型也足够提供高效的预测，前提是特征提取器经过良好训练。因此，这项研究有助于理解医学图像分类中的迁移学习，并为根据特定需求选择合适的模型提供了见解。
URL
https://arxiv.org/abs/2512.04397
