Fig 1.
Comprehensive workflow of the Elastic YOLO detection system.
Fig 2.
Representative ground-truth annotation samples from the curated peripheral blood smear dataset.
Table 1.
Dataset Composition and Annotation Statistics.
Table 2.
Descriptive Distribution of Annotations Per Image.
Fig 3.
Multi-scale blood cell detection using YOLOv5 architecture.
Fig 4.
Proposed Elastic YOLO design integrating adaptive scaling and deformable convolution modules.
Table 3.
The Workflow of the Elastic YOLO Model for Blood Cell Morphology Detection.
Table 4.
Per-class detection metrics for Elastic YOLO and Baseline YOLOv5.
Fig 5.
Detection results on test samples for YOLOv5 and Elastic YOLO.
Table 5.
IoU comparison between Elastic YOLO and Baseline YOLOv5.
Table 6.
AP comparison across multiple IoU thresholds.
Fig 6.
AP comparison at multiple IoU thresholds.
Fig 7.
Training and validation accuracy-loss trends across epochs for EYOLO and YOLOv5 models.
Table 7.
Epoch-wise comparison of training and validation metrics for Elastic YOLO and YOLOv5.
Table 8.
Epoch-wise analysis of individual and total loss for Elastic YOLO and YOLOv5.
Table 9.
Benchmarking Elastic YOLO against SOTA models on the dataset.
Fig 8.
Qualitative detection comparison of various models on a sample microscopy image.
Red: abnormal cells; green: normal cells.
Fig 9.
State-of-the-art model comparison.
(A) Evaluation metrics. (B) Computational complexity.
Table 10.
Ablation study results for Elastic YOLO.
Fig 10.
Ablation analysis results.
Table 11.
Performance of Elastic YOLO variants with limited training samples.
Fig 11.
(B) Generated diagnostic report.
Fig 12.
Failure cases with (A) Ground truth annotations.
(B) Elastic YOLO predictions.
Fig 13.
Confusion matrix illustrating classification performance of Elastic YOLO across six blood cell categories.