A Deep Learning Approach to Tumour Identification in Fresh Frozen Tissues The demand for pathology services are significantly increasing whilst the numbers of pathologists are significantly decreasing. In order to overcome these challenges, a growing interest in faster and efficient diagnostic methods such as computeraided diagnosis (CAD) have been observed. An increase in the use of CAD systems in clinical settings has subsequently led to a growing interest in machine learning. In this paper, we show the use of machine learning algorithms in the prediction of tumour content in Fresh Frozen (FF) histological samples of head and neck. More specifically, we explore a pretrained convolutional neural network (CNN), namely the AlexNet, to build two common machine learning classifiers. For the first classifier, the pretrained AlexNet network is used to extract features from the activation layer and then Support Vector Machine (SVM) based classifier is trained by using these extracted features. In the second case, we replace the last three layers of the pretrained AlexNet network and then fine tune these layers on the FF histological image samples. The results of our experiments are very promising. We have obtained percentage classification rates in the high 90s, and our results show there is little difference between SVM and transfer learning. Thus, the present study show that an AlexNet driven CNN with SVM and finetuned classifiers are a suitable choice for accurate discrimination between tumour and nontumour histological samples from the head and neck.

Automated Classification of Oral Cancer Histopathology images using Convolutional Neural Network In latest years, convolutional neural networks (CNNs) have accomplished stateoftheart performance in many computer vision tasks such as classification of images, object detection, instance segmentation etc. CNN has a robust learning capacity and can enhance the use of datasets for feature extraction. Identifying the key visual features from the oral squamous cells are the significant and compulsory task for the clinicians to detect the different stages of oral cancer. The computeraided instrument performing the same identifying job would provide clinicians with a vital guidance during diagnosis for evaluating histological images. In this study, we suggest the use of 4layered (5X5X3) patches of convolutional neural networks (CNNs) for feature extraction and classification from oral cancer images. To prevent overfitting the images were augmented by rotating, inverting and flipping. The proposed model has achieved 96.77 % accuracy, with 10 fold cross validation, which is at par with the accuracy of cytotechnologists and pathologists. Therefore, this model is helpful in classifying oral cancer microscopic images.