Deep Fully Convolutional Networks for Mitosis Detection

Image recognition plays a vital role in the medical image analysis field, which depends on different medical image analysis algorithms with input data, features, parameters, and type of learning. Three crucial morphological features on Hematoxylin and Eosin 1991 (H&E) related to the classifying the diseases for breast cancer, mitosis count, tubule formation and nuclear pleomorphic. Mitosis counts plays an essential role and an important diagnostic factor for breast cancer grading. Mitosis detection is still a challenging problem because the cells are part of the cell cycle to generate a new nuclear and with different stages of mitosis. However, we implemented a residual learning algorithm for optimization and easiest training; our model is ResNet18 pre-trained to classify with localized based on the Tensorflow framework (TF-DFCNN). Moreover, it is used for avoiding the degradation problem consisted of the normalization function, data augmentation and sampling method to get high accuracy detection. Our deep fully convolutional network (DFCNN) consists of two-stage, where the first stage is used for classification of MITOS-ATYPIA 2014 dataset, which achieves 85% accuracy. In the second stage, we add a new layer to detect the localization depends on Weakly-Supervised Object Localization Concept via a class activation map (CAM) technique for identifying discriminative regions to retrain our CNN model without fully connected layer by combining the framework with localized layer lead the model to be more complex and precise about 93% accuracy.