Classification of Acute Lymphoblastic Leukemia on White Blood Cell Microscopy Images Based on Instance Segmentation Using Mask R-CNN

Manual classification of acute lymphoblastic leukemia carried out by doctors will certainly take a lot of time and effort. The challenges in automated computer-based systems for classification of acute lymphoblastic leukemia are when providing proper lightning in stained white blood cell microscopy images and when segmenting the touching or overlapping cells in the image. Previous studies related to the classification of acute lymphoblastic leukemia still require many steps when using conventional methods, whereas when using deep learning methods are still limited to classification without providing analysis for the instance segmentation of lymphoblast in the image. Therefore, we propose instance segmentation using Mask R-CNN on white blood cell microscopy images to classify acute lymphoblastic leukemia that can support the diagnosis process efficiently and effectively. In this study, we implemented Mask R-CNN by transfer learning method to fit the instance segmentation task on white blood cell microscopy images. We added a contrast enhancement process to the image dataset to overcome the bad lightning problem in stained white blood cell microscopy images. We used the real dataset obtained from hospital to evaluate our method. The method we used was able to get 83.72 % accuracy, 85.17 % precision, and 81.61