# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 5 Experimental Results
### Results and Comparison with State-of-the-Art  
The performances of the proposed method were evaluated with the accuracy, precision, recall, and F-measure metrics, which according to the literature, are used most often for counting purposes. The results were obtained using leave-one-out cross-validation. It is essential to notice that the detection results were promising because the method produced only one False Negative (FN) and correctly identified 270 cells (out of 271) arranged in the first 33 images of ALL-IDB. In detail, 416 objects were detected. Then, the recognition step eliminated the cells touching the borders; therefore, the count decreased from 416 to 345. It identified correctly 270 cells (TP), misidentified one WBC (FN), and recognized correctly 74 artifacts (TN). We compared the performances of the proposed method with those obtained by other authors who used ALL-IDB to test their approaches for WBC counting. Figure 9: Sample images extracted from the described datasets. From left to right: ALL-IDB1, SMC-IDB, and IUMS-IDB sample images. It must be noted that several authors attempted to count WBCs in ALL-IDB; they are indicated in Table 1 and compared to our results. In particular, [PERSON] [7] performed a color space conversion and then used the CHT with no restrictions on the area of interest. This method was only tested on ten images. On the contrary, [PERSON] [22], [PERSON] [23] and [PERSON] [24] applied the CHT after a segmentation phase to restrict the search to a specific area of interest. They completed the counting on the first 33 images and 14 random images, respectively. In detail, [PERSON] [23] segmented the images using a fixed threshold over the G channel of the RGB color space. In contrast, [PERSON] [24] segmented the images using a machine learning approach that combined nearest neighbor and support vector machine. [PERSON] [6] combined the distance transform with the watershed algorithm to separate the agglomerates produced by the segmentation step. They both worked on the first 33 images of ALL-IDB1. Finally, [PERSON] [9] used a sliding windows strategy to classify each candidate bounding box using hand-crafted features; he tested his method on an unknown number of images. To further assess the performance of the proposed segmentation algorithm, we computed the pixel-wise accuracy by comparing our results with the ground truth images. Some interesting results are presented in Figures 10 and 11. In particular, the first one was subdivided into intervals spaced 0.25% and showed that more than 90% of the cells were segmented with a pixel-wise accuracy higher than 95%. Moreover, it must be noted that 256 objects (out of 271) were segmented with an accuracy higher than 90%. While Figure 10 analyses in detail the accuracy of the objects, Figure 11 focuses on the segmented images. It shows the pixel-wise accuracy for each one and a red line that indicates the average accuracy of 97.02%. However, it is essential to specify why some images had a lower accuracy than the average. For example, in Image Numbers 9 and 30, which are depicted in Figure 12, the segmentation of the leukocytes inside the red rectangle was harder due to the presence of a light WBC's cytoplasm. Due to this, the method was not accurate in recognizing all the pixels of the cell, even though the overall accuracy remained higher than 90% for both cases. The proposed approach also improved the health status classification results; in particular, it obtained an accuracy of 94.1%, improving [PERSON]'s [6] results, which obtained 93.2% for the same task in the same environment setting. We also performed two further detection experimentations on IUMS-IDB and SMC-IDB depicted in Section 5, datasets composed of images with very different characteristics from ALL-IDB. The final result for WBC detection was 100% in both cases and matched the performances achieved by [PERSON] [22].