Deep Learning-Based Multi-Class Body Fluid Cell Type Classification: A Comparative Evaluation of Image Enhancement Techniques
TL;DR
Class-wise results showed strong performance for distinct cell types, while macrophages, monocytes, and lymphocytes remained challenging, and selected enhancement techniques offer modest improvements, but effectiveness depends on model architecture and data characteristics.
Abstract
Accurate classification of cells from body fluid specimens can support cytological analysis, but microscopic images often present challenges such as low contrast, unclear boundaries, staining variation, class imbalance, and overlapping morphology. This study evaluated the impact of image enhancement techniques on deep learning-based multi-class classification of body fluid cells. The dataset comprised 7071 microscopic images from Srinagarind Hospital, Thailand, annotated by expert technologists. After preprocessing and cell extraction, 22,062 single-cell images across 13 cell types were obtained. A 70:30 train–test split was used, with augmentation and random undersampling applied only to the training set. Nine individual enhancement filters and five combined settings were tested using MobileNetV3, DenseNet121, ResNet50, and EfficientNetB3. Performance was measured using weighted accuracy, precision, recall, and F1-score. DenseNet121 with Edge Enhance achieved the best individual-filter performance (accuracy 83.36%, F1-score 83.08%). For combined settings, DenseNet121 with CLAHE followed by Detail performed best (accuracy 83.01%, F1-score 82.54%), though it did not surpass the top individual filter. Multi-step enhancement provided limited additional benefit. Class-wise results showed strong performance for distinct cell types, while macrophages, monocytes, and lymphocytes remained challenging. Grad-CAM visualization further indicated that model attention was generally concentrated on relevant cellular regions, including nuclei, cytoplasm, and cell boundaries. Overall, selected enhancement techniques offer modest improvements, but effectiveness depends on model architecture and data characteristics. As this study used a single-institution dataset without external validation, the approach should be considered an assistive framework rather than a clinically validated diagnostic system.