Cell classification framework using U-Net: convolutional networks for cervix cell segmentation

With the technological advancements in machine learning, it has become more prevalent to use learning techniques for clinical decision-making based on medical images. One of the state-of-the-art methods used for this purpose is Convolutional Neural Networks (CNN) for medical image segmentation and deep learning models for disease detection and classification. In this paper, we propose a framework for image segmentation using hierarchical CNNs to classify different types of cells using small frame images. This paper aims to generalize the segmentation of cancer cells, starting with cervix cancer. The first step of the framework is to achieve automatic nucleus and cell masking of the images using U-Net. The images are then segmented into “satisfactory” and “unsatisfactory” categories to determine whether these images can be used in our classification model. Using the hierarchical CNN, the satisfactory images are clustered based on cell types since the cell features that need to be considered vary between different cell types. Lastly, our classification model is trained with automatically segmented images to classify different cancer types based on cell images using various features, such as the area of the nucleus, the ratio of the nucleus area and cytoplasm area and the visual morphology of chromatin strands in the nucleus. To demonstrate the performance of the proposed framework, a labeled dataset, taken from the Detay Pathology and Cytology Laboratory, with over 100 images were used.