###### Abstract  
Recently, digital pathology is an essential application for clinical practice and medical research. Due to the lack of large annotated datasets, the deep transfer learning technique is often used to classify histopathology images. A softmax classifier is often used to perform classification tasks. Besides, a Support Vector Machine (SVM) classifier is also popularly employed, especially for binary classification problems. Accurately determining the category of the histopathology images is vital for the diagnosis of diseases. In this paper, the conventional softmax classifier and the SVM classifier-based transfer learning approach are evaluated to classify histopathology cancer images in a binary breast cancer dataset and a multiclass lung and colon cancer dataset. In order to achieve better classification accuracy, a methodology that attaches SVM classifier to the fully-connected (FC) layer of the softmax-based transfer learning model is proposed. The proposed architecture involves a first step training the newly added FC layer on the target dataset using the softmax-based model and a second step training the SVM classifier with the newly trained FC layer. Cross-validation is used to ensure no bias for the evaluation of the performance of the models. Experimental results reveal that the conventional SVM classifier-based model is the least accurate on either binary or multiclass cancer datasets. The conventional softmax-based model shows moderate classification accuracy, while the proposed synthetic architecture achieves the best classification accuracy. image classification; support vector machine; transfer learning +
Footnote †: journal:  
Support Vector Machine (SVM) is a supervised learning model initially designed for binary classification tasks [12; 13] and has high-quality generalization ability for binary classification problems [14; 15]. SVM can be extended to solve multinomial classification problems employing Error-Correcting Output Code (ECOC) [16; 17; 18]. The outcome is, however, not as good compared with binary problems. SVM is also actively used as the classifier in deep transfer learning. In [2], different deep transfer learning strategies for digital pathology are comprehensively compared on eight classification datasets, where SVM shows promising results when used as the classifier. However, some other researchers claim the advantage of SVM on binary problems is not always evident. The study in [19] tests three backend deep learning architectures, namely VGG, ResNet, and Inception, as feature extractors and three different classifiers, namely FC multilayer, SVM, and Random Forests, on four datasets in digital pathology, and evaluates reproducibility by examining the issue of evaluating the accuracy of predictive models. The emerging AI-based computational pathology has shown great promise in increasing the accuracy of high-quality health care, and contributes insights to the diagnosis and treatment of diseases [20]. Deep learning has been used for analysis of histology images, tumor detection, grading and subtyping, prediction of mutations, survival and response from histology, and largely automates clinical workflows [21]. In this paper, a transfer learning methodology combining the FC layer trained by a softmax classifier on the target dataset with the SVM classifier is proposed to classify histopathology images. Due to the shortages of the specific publicly available annotated histopathology dataset, some previous researches use transfer learning based on off-the-shelf deep CNN architectures, pre-trained on ImageNet as the backbone for classifying histopathology images. A softmax classifier is commonly used in transfer learning which is effective for binary or multinomial classification. An SVM classifier is, however, usually confined to binary classification. The proposed methodology contains a two-step procedure.