# Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network
## 4 Discussion  
Accurate diagnosis of the ear diseases requires mastery of the otoscopic examination [1]. Interpretation of the TM requires extensive training and experience with an associated learning curve [16]. It has been reported that pediatricians and otolaryngologists provide an accurate diagnosis of middle ear pathology in only 50% and 73% cases, respectively [17]. Some studies reported a diagnostic accuracy among otologists of 72-82% [18,19]. In some studies, diagnostic systems for AOM and OME rely on selected features used for image processing of otoscopic images. A diagnosis support system analyzing the color of the TM has been reported to achieve an accuracy of 73.11% [13]. Another diagnostic system using features that mimic an otologist's decision-making process for otitis media has been reported to achieve an accuracy of 89.9% [14], while an automated feature extraction and classification system for AOM and OME has achieved an accuracy of 91.41% [12]. While many studies on AOM and OME have demonstrated excellent results, image classification studies on COM are rare since COM can show variable lesions and anatomical distortion, making feature selection more complicated. [PERSON] et al. reported a computer-aided diagnostic system for otitis media [15]. They classified otitis media into four categories (normal, AOM, OME and COM) and the accuracy was 88.06%. The authors used different types of filters to extract features. In contrast, we extracted features using a deep neural network to characterize the TM in this study. These features were automatically applied for classification. We found that the proposed CNN model can improve diagnostic accuracy. Our model achieved the overall accuracy of 98.7% for the TM side and 91% for the presence or absence of perforation. These results are comparable with other diagnostic accuracies achieved by physicians, algorithms and machine-learning models. However, our CNN model was trained to perform binary classification only; its ability to distinguish various lesions is yet to be verified. To interpret the process, a CAM visualizing the extracted characteristics was used. In this study, we were able to obtain a CAM that can be analyzed in 8.7-94.8% of the test set. For TMs with perforation, the decision process of detecting perforation may be made by observing the perforation margin or middle ear, and the results were statistically significant. However, for the left normal TM, it is difficult to interpret whether the CNN shows such an accuracy even though the activation rate is very low. In particular, when the side is misjudged, the activation rate is relatively high (69.6%). Despite the fact that the normal structures usually used for the decision-making process by otologists were activated, some structures (TM anterior-superior, annuls anterior-superior, umbo) demonstrated negative correlation. Negative correlation was also observed for some subsites (EAC anterior-inferior, EAC posterior-superior, TM anterior-inferior) in the right normal TM; however, the other subsites were not significant. By employing deep learning in this study, we sacrificed the interpretability to achieve high accuracy and end-to-end training. CAM is one of the popular methods for interpreting CNN results. To overcome the limitations of CAM, several variations of the pooling method were reported, including global average pooling, gradient-based localization method and gradient-weighted CAMs [20]. Also, the bounding box generation has been proposed, which is a segmentation and covering method for connecting components to CAM values [21]. However, interpretability still remained a problem. The EAC was angled even in normal subjects. In general, the pinna should be manipulated in otoscopic examination to attempt aligning the cartilaginous portion of the ear canal with the bony portion. For better visualization of the TM, holding an otoscope in the left hand for examining the left ear and the right hand for the right ear is recommended [22].