Abstract
Blood cell analysis plays a key role in clinical diagnosis and hematological research. The accurate identification and quantification of different blood cell types is essential for the diagnosis of various diseases. The conventional manual method of blood cell analysis is both laborious and time-consuming, highlighting the need for automated segmentation techniques. In this paper, the blood cell segmentation problem is considered as a multi-class segmentation problem to detect the different types of blood cells in a given image. Two new multi-objective lightweight neural architecture search (NAS) algorithms (MOLiNAS) are designed to tackle the challenge of whole-slide multi-class blood cell segmentation problems. Our approaches integrate the most advantageous aspects of different approaches to search for the best U-shaped network architecture. The performance of our approaches is compared with lightweight networks and NAS studies in the literature. Our best solution (MOLiNASv2_sol3) achieves an IoU of 87.33 ± 1.53%, F1 score of 91.69 ± 1.20%, Precision of 93.50 ± 1.15%, and Recall of 91.34 ± 0.01%, outperforming lightweight networks such as EfficientNet, MobileNetv2, and MobileNetv3 across all segmentation metrics. Moreover, our approaches demonstrate highly competitive performance by utilizing up to 7.38 times fewer FLOPs and up to 4.03 times fewer trainable parameters than existing NAS studies while requiring only 0.07 million parameters. Additionally, ablation studies and cross-dataset evaluations demonstrate the robustness and generalizability of our approach.