The investigation of transferability utilizing the ImageNet weight-based pretrained model for medical image classification: A case study on kidney CT images

Abstract

Due to recent growth in technology, machine learning has emerged to be an effective auxiliary tool in medical field. However, the effectiveness of transfer learning architectures trained on non-medical image data remains unclear. In this paper, two VGG-16 models, a type of pre-trained Convolutional Neural Network architecture, were constructed to classify kidney CT images that belong to four categories: normal, stone, cyst, and tumor. Two VGG-16 models have identical parameters except for the pre-trained weights: one has pre-trained weights trained on ImageNet, and the other one trained on a random large-scale dataset. To gather a more detained insight into models performances, saliency maps and Grad-CAM are employed to assess the models' ability to extract relevant features from the CT images. The result demonstrated that VGG-16 model that is trained on ImageNet can achieve 98.96% accuracy, which is about 30% higher than the other VGG-16 model. The saliency maps and Grad-CAM also support the difference in test accuracy: the model with random pre-trained dataset has saliency map that highlights the whole picture and Grad-CAM image that does not highlight any part of the CT image data, showing that it cannot correctly locate the key features. Additionally, the model with ImageNet can correctly highlight the principal features in both maps. In this study, the utilization of ImageNet is proven to be effective in the usage of transfer learning in processing medical image. Future research and exploration should focus on further enhancing the application of transfer learning in the medical field.