Author | Method | Types datasets | Results Obtained |
---|---|---|---|
P. A. Nogueira et al. [18] | Otsu thresholding; SVM classifier | Fluorescence microscopic image | 85.3% classification accuracy |
M. Farahi et al. [19] | Preprocessing (contrast stretching, masking); Segmentation (Modified Chan-vese (CV) Level Set Method) | Geimsa stained Microscopy images | Segmentation error of 10.9% using global CV and 9.76% using local CV |
J. C. Neves et al. [20] | Blob detection based classification | Fluorescence microscopic image | Average F1 score of 84.48% |
F. Ouertani et al. [21] | Watershed segmentation technique combined with region merging | Fluorescence microscopic image | NA |
M. Górriz et al. [16] | U-net model classification | Florescence-stained microscopic image | NA |
E. Yazdanparast et al. [24] | INsPECT (infection level measurement); Preprocessing (morphological filtering) | Fluorescent DNA staining | NA |
M. Zare et al. [22] | Machine learning based Leishman parasite detection | Geimsa-stained microscope images | Precision 50% recall 60% |
Proposed Model | Object detection with YOLO_V5 | Geimsa-stained microscope images | MAP 73%, Precision 68%, Recall 69% |