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.

Texturebased Deep Learning for Effective Histopathological Cancer Image Classification Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time and costconsuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texturebased deep neural network (CATNet) that learns scalable morphological features from histopathological WSIs. The innovation of CATNet is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CATNet can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CATNet, captures morphological patterns of interest in hierarchical levels in the model. The proposed method outperformed the current stateoftheart benchmark methods on accuracy, precision, recall, and F1 score.