## 4 Discussion
From the results, we attempted to reveal the effectiveness of color information in bacillus detection, which could help pathologists gradually realize how the model works and accept its use in clinical situations. In the past, the main challenge for WSI studies was extremely large image sizes. For example, one of the collected WSIs is as large as 51,757 \(\times\) 12,624 pixels. In this study, the whole slides were separated into blocks with the size of 20 \(\times\) 20 pixel for the training of the detection model. A smaller size cannot show the complete morphological properties while a bigger size cannot focus on the target due to too many background tissues. In clinical use, blocks compose the whole slide can be evaluated by the trained model and be given a probability being a bacilli. Then, blocks with higher probabilities are placed prior to those with low probabilities for pathologists to observe. By this way, the pathologist can have the potential to find a bacilli faster. Once a block with high probability having bacilli is examined as having true bacilli by a pathologist, no further examination is required. Such as an example having the original order: 0.1, 0.3, 0.2, 0.4, 0.6, 0.2, 0.9 and the modified order: 0.9, 0.6, 0.4, 0.3, 0.2, 0.2, 0.1 can have chance to detect a bacilli (probability = 0.9) at the first block rather than the original seventh block. The use of whole-slide pathology images can be promising in future applications. However, few related studies could be found that had a limited sample size and that could perform deep learning with limited computational resources. Consequently, this study proposed to use transfer learning to solve the problem of limited samples and to use AlexNet to show that simpler layers of convolutional neural networks could still perform well with less computational power and time. We believe that the results shown in the manuscript have demonstrated this possibility and can provide suggestions to many future studies focusing on using limited medical images for deep learning. Additionally, it will be meaningful for clinical use. Other DCNN architectures for image classification, such as Inception-v3 [43], ResNet-101 [44], and DenseNet-201 [45], were also explored. The accuracy of bacillus detection for these architectures were 93%, 94%, and 95%, respectively, comparing well to AlexNet (95%). All of them achieved promising accuracy in bacillus detection with very subtle differences among the performances. When considering the use of these architectures in real clinical situations, it should be noted that the complicated ResNet-101 and DenseNet-201, each containing substantial numbers of layers, have high computational costs and may therefore be impractical. In the clinical use, specific circuit would help accelerate the detection time [46]. The first limitation to future development is the lack of a universal, standardized scanning system for digital pathology. A full histopathology digitalization system was recently approved by the US Food and Drug Administration for primary diagnosis [11]. Consensus on WSI scanning preparation would provide guidelines for future studies. Second, the various scanning machines and histopathological staining quality may differ among hospitals. When collecting additional cases for future studies, more pathological images from multiple centers with different staining qualities are necessary for building a comprehensive prediction model. Additionally, if enough cases are acquired, the whole image can be used for testing to simulate the real clinical use of the system and evaluate whether the proposed system can improve reader performance. Then, the proposed CAD could be generalized to other diagnosis tasks and pathogens, such as deep fungal cutaneous diseases and parasitic infectious disease. With advances of radiomics, future work may include the integration of radiological and histopathological images.