## 2 Materials and Methods
### Effects of Color Information  
Due to enormous variations in the color and structural layouts of bacillus-negative image areas, three groups of bacillus-negative block sets were selected with the same positive block set mentioned above to evaluate the proposed CAD system. Acid-fast positive staining showed reddish colors with a bluish background. In addition to mycobacterial bacilli, other tissue was stained a reddish color as well. For example, hair shaft cortices stain a splendid reddish color and appear positive for bacilli. On the other hand, granules within mast cells stain a faint reddish color. Bacillus-negative images with reddish colors were subclassified into splendid and faint color groups according to the above criteria  
\begin{table}
\begin{tabular}{c c c} \hline
**Disease Type** & **Number of Cases** & **Histopathological Features** \\ \hline Tuberculosis and atypical mycobacterial infection & 5 & \begin{tabular}{c} Granulomatous inflammation with multinucleated giant \\ cells, surrounded by lymphocytes and plasma cells. Caseous \\ necrosis and mixture of neutrophils and lymphocytes. \\ Microcysts lined by neutrophils. \\ \end{tabular} \\ \hline Paucbacillary leprosy & 1 & \
\begin{tabular}{c} Sausage-shaped granuloma formation, mixtures of \\ lymphocytes and macrophages \\ \end{tabular} \\ \hline Lepromatous leprosy & 2 &
\begin{tabular}{c} Perivascular lymphophistiocytic infiltration, foamy histiocyte \\ aggregation, sheets of histiocytes with grenz zones \\ \end{tabular} \\ \hline Erythema nodosum & 1 & Leukocytoclastic vasculitis, onion skin fibrosis around nerves \\ leprosum & & \\ \end{tabular}
\end{table}
Table 1: Histopathological features of the nine selected skin incisional biopsy tissue samples.  
(Figures 2 and 3, the magnification is used to illustrate the local pattern rather than the actual size). First, the splendid group showed that negative images with a splendid reddish color may be misclassified as positive. Examples among the 1022 negative image blocks showing splendid reddish-stained features such as hair follicles, corneal layers of the epidermis, foreign bodies, and amorphous background stains are shown in Figure 2.  
Next, negative image blocks with a faint reddish color and structures with similar appearances to positive bacilli were extracted. These image blocks represented a great clinical challenge to physicians for differentiating from bacilli. A total of 1383 negative image blocks composed of sebaceous glands, infiltration of mast cells, granules from mast cells, and background stain that showed silk-like features with a faint reddish color were collected as the faint group (Figure 3).  
Finally, a grayscale image experiment was proposed to evaluate the overall contribution of color features to the success of the proposed convolutional neural network. In this group, the 1202 negative image blocks were first selected from the previous two groups. Then, both the original color samples  
Figure 1: Acid-fast stained bacilli samples cropped as positive blocks (20 × 20 pixels).  
Figure 2: Reddish and violaceous tissues collected as negative samples in the splendid group.  
and the grayed samples were prepared for model training and testing, as shown in Figure 4. For grayed samples, only morphological structure information and relative intensities compared to surrounding tissues could be used. The result of this experiment would further illustrate the contribution of color information used in stained bacillus detection and make the performance of the diagnostic method more promising for clinical use.  
Figure 4: Color and grayscale training samples. The left group is positive blocks and the right group is negative blocks.  
Figure 3: From left to right and top to bottom to have **A**–**I** images, they are contamination (images **A**, **F**), hair follicle and hair shaft (images **B**, **H**), corneal layer of epidermis (images **C**, **D**), amorphous background staining (images **E**, **G**, and **I**) collected as negative samples in the faint group.