The transferability of transfer learning model based on ImageNet for medical image classification tasks

Abstract

Transfer learning with pretrained weights is commonly based on the ImageNet dataset. However, ImageNet does not contain medical images, leaving the transferability of these pretrained weights for medical image classification an open question. The core purpose of this study is to investigate the impact of transfer learning on the accuracy of medical image classification, utilizing ResNet18, VGG11, AlexNet, and MobileNet, which are four of the most widely used neural network models. Specifically, this study aims to determine whether the incorporation of transfer learning techniques leads to significant improvements in the performance of image classification tasks, as compared to traditional methods that do not utilize transfer learning. The dataset consists of approximately 4,000 chest X-ray images with labels of healthy, COVID, or Viral Pneumonia. The final layer's output neurons of the networks architecture were revised to three to accommodate the ternary classification task. Preprocessing techniques include downsampling and normalization of the pixel values. By maintaining the same dataset and preprocessing methods, this study compares the performance of the models with and without pretrained weights. The results demonstrate that, compared to not using transfer learning, all four network models converge more quickly and achieve higher validation accuracy in the initial epochs when transfer learning is employed. Furthermore, the models exhibit higher prediction accuracy in the final test set. This study suggests that using transfer learning with pretrained weights based on ImageNet can boost the efficiency of medical image classification tasks.