Hybrid Techniques for the Segmentation and Classification of White Blood Cancer Based on the Fusion of Deep Network Features
Year: 2026
Type: article
Abstract: Microscopic diagnosis of acute lymphoblastic leukemia (ALL) and multiple myeloma (MM) depends on expert review, and the public datasets available for automated analysis are small and imbalanced. This study presents a four-stage pipeline for nucleus segmentation and cell classification on the SN-AM and SegPC-2021 datasets. Images were enhanced with contrast-limited adaptive histogram equalization (CLAHE); a conditional generative adversarial network (pix2pix) generated 2500 synthetic image–mask pairs per dataset from new nucleus layouts; nuclei were segmented with UNet using five pre-trained CNN encoders and compared with YOLO26; and single cells were classified with XGBoost on fused, PCA-reduced deep features. Models were trained with and without synthetic data and evaluated on real test images only. The synthetic pairs improved UNet on SN-AM (Dice 0.934 to 0.951 for EfficientNetB7, six test images), whereas on SegPC-2021 their effect was within ±0.01 Dice; YOLO26 gave the best SegPC-2021 result (Dice 0.799). Fused features classified ALL and MM cells with a balanced accuracy of 0.978–0.984. However, background patches without leukocytes also identified the source dataset (ROC-AUC ≥ 0.996); in a same-source experiment, three fused feature sets still reached 0.993–0.996. Synthetic data helped most where annotations were scarce; larger same-source datasets are needed for confirmation.
DOI: 10.3390/mca31060253
Authors: Annal A. Albeeshi, Hanan S. Alshanbari
Institution: Umm al-Qura University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,689
OpenAlex ID: W7225659319