Co-occurrence feature learning for visual recognition of immature leukocytes

Abstract

Abstract Accurate and timely diagnosis of leukemia, a cancer characterized by an excessive number of abnormal white blood cells (WBCs), is crucial for effective treatment. Manual examination of blood smear images for leukemia diagnosis is often laborious and costly. Computer-aided classification of WBCs has the potential to assist hematologists in improving diagnostic accuracy. However, the subtle visual differences among the five types of immature neutrophils pose a significant challenge, even for experienced professionals. The study proposes a method called densely connected co-occurrence network (DCONN). The method first detects white blood cells in blood smear images using Yolact. Then, the images are pre-processed to minimize the correlation between image channels by transforming RGB color space to LAB color space. Finally, DCONN extracts spatial texture information using a co-occurrence matrix to improve classification accuracy. DCONN achieved 93.46% accuracy in classifying five types of immature neutrophils: myeloblast, promyelocyte, myelocyte, metamyelocyte, and band cells. The results indicate that using a combination of densely connected convolutional layers and a co-occurrence layer improves classification accuracy while using fewer trainable parameters than other deep learning methods such as ResNet and Inception. Additionally, the model is less demanding in terms of training hardware than attentional mechanism-based models that also have local feature representation. DCONN achieves advanced performance based on small-scale models without requiring much training time. The proposed method can be extended to other pathological image analyses in the future.

Keywords

• Leukocytes
• Co-occurrence matrix
• DenseNet
• Yolact
• Leukemia