A blood cell dataset for lymphoma classification using faster R-CNN
Biaosheng Sheng, Mei Zhou, Menghan Hu, Qingli Li, Sun Li, Ying Wen
Abstract
Lymphoma has become the seventh most common cancer expected to occur and the ninth most common cause of cancer death in both males and females. However, pathological diagnosis as the main diagnostic method is time-consuming, expensive and error-prone. Nowadays, with the outstanding performance of Convolutional Neural Network (CNN) in image analysis, its application in medical image classification and segmentation is becoming more and more widespread. Since Faster R-CNN achieves state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets, in this work, we use it to identify lymphoma cells from a dataset of blood cells. In order to achieve this goal, first, we focus on a new blood cell dataset that mainly consists of lymphoma cells, blasts and lymphocytes. This dataset will be used to fine-tune a pre-trained network. Then, we use two training methods and three networks to train the same dataset respectively. Finally, we choose the best trained model to diagnose lymphoma cells in a new dataset which also contains lymphoma cells, blasts and lymphocytes. The detection rate of lymphoma cells was higher than 96%, and the false detection rate was less than 13%, which is an improvement compared with the previously proposed results. The results show a potential of the proposed method in lymphoma diagnosis.