## 4 Discussion  
The conventional CAD system is a well-established diagnostic tool in the medical image field. The procedures it is used for usually include segmentation, feature extraction, and classification [20]. CAD systems based on numerous image processing algorithms have already been developed for digital pathology slides, especially for laborious tasks such as detection and quantification in pattern recognition. The primary goal of CAD systems is to increase sensitivity while reducing false negative rates due to fatigue of the observers [16; 21]. Mycobacterium infection histopathology remains a diagnostic challenge because it costs physicians time and effort to look for bacilli under the microscope. Numerous CAD systems for automatic backills detection have been developed since the 1990s [34]. Previous CAD systems identified mycobacteria bacilli following image segmentation and feature extraction defined by the physician. Published methods use segmentation based on color thresholding followed by the classification of sizes and shapes. Stained positive bacilli are reddish, while the counterstained background is blue, and the shape of the bacillus is expected to be rod-like [21]. Although the rules of the morphological features of bacilli could be described by trained pathologists, developing algorithms for identifying bacilli is a difficult task, as the bacillus shape and orientation vary under the microscope. Therefore, using handcrafted features for classification is intuitive, but developing algorithms is challenging. For the past few years, research on mycobacterial backillus detection has focused on sputum smears and cytology specimens. Computerized workflows have been developed to identify the bacilli from sputum smear images [34; 35; 36; 37; 38; 39; 40], but few studies have focused on histopathological sections. Biopsy tissue samples of mycobacteria-infected organs remain critical for diagnosis, especially for extrapulmonary mycobacterium infectious diseases [5]. Furthermore, sputum smears and tissue sections differ in several ways. First, a rod-like bacillus is expected to have a fluid-like shape under the smear pressure. In tissue sections, bacilli are suspended in paraffin wax in different orientations. This results in variations of bacillus shapes from rod-like to point-like under different levels of tissue sections. Previous studies used tissue images captured from cameras on light microscopes with limited resolutions compared to whole-slide scanning [41; 42]. In the experiments, the proposed DCNN yielded both good sensitivity and specificity regardless of the kind of negative image blocks compared with true bacillus-positive image blocks. This result indicates that even with respect to the dying issues, the classifier showed a high accuracy of 95.3%. The pretrained CNN model, which learned image features from natural images, performed well in classifying pathological images of acid-fast stained mycobacteria bacilli. Particles stained reddish to violaceous may simulate positive acid-fast stain bacilli. For instance, contamination particles, background stains, hair shafts and the corneal layer of the epidermis stain reddish and sometimes show fragment-like morphologies. Sebaceous gland tissues with foam-like structures that simulate bacillus morphology under high resolution could also confuse observers. After the image blocks were transformed into grayscale, the performance dropped to 73.8% accuracy. The possible reason for this could be that color information is essential for bacillus detection. Nevertheless, the remaining morphological properties and intensities still provided approximately 73.8% accuracy. This result implies that morphological features and sharpness are also crucial factors for detection. Similar to past studies, shape analysis of mycobacterial infectious images was implemented in the CAD system.