CELL DETECTION USING DEEP LEARNING AND HAND-CRAFTED FEATURES

Abstract

With the Artificial Intelligence becoming more and more powerful in time, there is a growing interest about scientists focusing in providing answers in the field of medical image analysis. The importance of using AI in the medical field is of great significance for many reasons. Robust algorithms are able to help physicians in processing large amounts of data, assisting in providing diagnosis and mitigating human error while reducing time and cost. This is especially applicable in the case of cell image analysis. Considering the complexity of cells as entities, which derives from their shape and form, it can be quite challenging to detect them in different settings that they are placed. An automated process on cell detection will be very beneficiary for anatomopathologists. In this work, there is cell detection performed, using the state-of-the-art deep learning object detection model, Faster R-CNN and trained on hand-crafted features such as Local Binary Patterns. The dataset used portrayed several challenges that most microscopy images hold, which required histogram and template matching for preprocessing. With cell detection performed, we also count the cells in an image by nuclei localization. There were several experiments conducted that achieve up to 56% mAP.