Examination of blood samples using deep learning and mobile microscopy

BMC Bioinformatics

Table 2 mAP and mAR at IoU’s of 0.50 to 0.95 for the validation (v) and test set (t)

Framework	Dataset	Ø mAP ± σ				Ø mAR ± σ
Framework	Dataset	All	RBC	WBC	PLT	All	RBC	WBC	PLT
Mask R-CNN	v	0.43 ± 0.02	0.46 ± 0.02	0.68 ± 0.06	0.15 ± 0.01	0.46 ± 0.02	0.48 ± 0.02	0.72 ± 0.06	0.18 ± 0.02
Mask R-CNN	t	0.47 ± 0.01	0.60 ± 0.01	0.65 ± 0.02	0.16 ± 0.02	0.50 ± 0.01	0.63 ± 0.01	0.67 ± 0.02	0.18 ± 0.02
MS R-CNN	v	0.67 ± 0.01	0.53 ± 0.02	0.91 ± 0.01	0.56 ± 0.00	0.69 ± 0.01	0.54 ± 0.02	0.93 ± 0.01	0.59 ± 0.02
MS R-CNN	t	0.48 ± 0.02	0.58 ± 0.01	0.57 ± 0.06	0.28 ± 0.01	0.53 ± 0.01	0.63 ± 0.01	0.61 ± 0.05	0.36 ± 0.02
D2Det	v	0.42 ± 0.04	0.56 ± 0.02	0.46 ± 0.05	0.24 ± 0.07	0.47 ± 0.04	0.60 ± 0.02	0.51 ± 0.05	0.30 ± 0.07
	t	0.44 ± 0.01	0.61 ± 0.02	0.45 ± 0.01	0.25 ± 0.03	0.49 ± 0.01	0.65 ± 0.02	0.50 ± 0.01	0.33 ± 0.04
YOLACT	v	0.45 ± 0.03	0.42 ± 0.02	0.71 ± 0.08	0.24 ± 0.05	0.49 ± 0.03	0.44 ± 0.02	0.74 ± 0.07	0.28 ± 0.09
	t	0.50 ± 0.01	0.56 ± 0.02	0.67 ± 0.02	0.28 ± 0.03	0.55 ± 0.00	0.60 ± 0.01	0.69 ± 0.02	0.37 ± 0.04

IoU’s of 0.50 to 0.95 with maximum of 100 detections for trained Mask R-CNN, MS R-CNN, D2Det and YOLACT for the validation and test set

ISSN: 1471-2105