Transfer Learning from Non-Medical Images to Medical Images Using Deep Learning Algorithms

Abstract

Introduction: Machine learning, especially deep convolutional neural networks (DCNNs), is a popular method for computerizing medical image analysis. This study aimed to develop DCNN models for histopathology image classification utilizing transfer learning.Material and Methods: We utilized 16 different pre-trained DCNNs to analyze the histopathology images from the animal diagnostic laboratory (ADL) database. During the image preprocessing stage, we applied two methods. The first method involved subtracting the mean of ImageNet images from all images. The second method involved subtracting the mean of histopathology training images from all images. Next, in the 16 pre-trained networks, feature extraction was done from their final six layers, and the features extracted from each layer were fed separately into the linear and non-linear support vector machine (SVM) for classification.Results: The results obtained from the ADL database show that the classification rate in lung tissue images is much better than that of the kidney and spleen. For example, the lowest detection rate in non-linear SVM for lung tissue is 14.96%, almost close to the highest accuracy in kidney and spleen tissue. The classification accuracy of the spleen images is better than that of the kidneys, with only a slight difference. In linear SVM on lung images, ResNet101 obtained the most accurate result with a value of 99.56%, followed by ResNet50, ResNet152, VGG_16, and VGG_19. In non-linear SVM on lung tissue images, the ResNet101 network with 99.65% and ResNet50 with 99.21%, followed by ResNet152, VGG_16, and VGG_19 obtained the highest detection rate.Conclusion: The classification results obtained from different methods on the ADL (including kidney, spleen, and lung histopathology images) database, confirmed the validity of transferring knowledge between non-medical and medical histopathology images. Additionally, it demonstrates the success of combining classifiers trained on deep features. This research obtained higher accuracy in the ADL database than the works done.