# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 7 Endoscopy
### Miscellaneous  
In the research work of [PERSON] et al. [47], they propose an automated capsule endoscopy video summarizing framework by combining deep and handcrafted features. For feature extraction, a pretrained GoogleNet is used. Hence, the deep features do not overfit the endoscopy images. Five hundred sample images are chosen from the KID [28] by a gastroenterologist and GivenImaging capsule videos from different parts of the colon. [PERSON] et al. [48] investigated using the state-of-the-art FlowNet algorithm for motion estimation in ocular endoscopy videos. Because FlowNet is strongly supervised, an artificial dataset of consecutive images paired with ground-truth optical flow was generated using eye fundus photographs from [PERSON]'s Diabetic Retinopathy Detection dataset [49]. A review paper discussed deep learning and non-deep-learning techniques applied to endoscopy videos to detect and classify polyps [50]. According to the authors, the main challenges were the presence of noninformative frames in wireless endoscopy videos and difficulty in real-time detection due to time complexity. Another study surveyed research articles for cancer detection using deep learning techniques in WCE videos [51]. The authors discussed several challenges in this field: a lack of annotated data and standard evaluation methods for the model's performance. The authors also suggested that for future research, domain knowledge and real-time diagnosis should be taken into consideration. The research articles reviewed for endoscopy video analysis are summarized in Tables 4-6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Research Paper** & **Purpose** & **Method** & **Database** & **Result** & **Limitations** & **Advantages** \\ \hline \multirow{4}{*}{[PERSON] et al., 2017 [19]} & \multirow{4}{*}{Detection of polyps} & 3D CNN and 3D FCN (both spatial and temporal features are extracted) & ASU-Mayo Clinic & \multirow{4}{*}{Precision = 1.0, F1 = 99.2\%, F2 = 98.7\%} & \multirow{4}{*}{Long processing time} & \multirow{4}{*}{Higher F1 and F2 scores compared with previous works} \\ \cline{5-6}  & & 3D CNN with iterative cluster & & & & Deep saliency detection \\ \cline{1-1} \cline{5-6}  & & 3D CNN with & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of contributions where the detection task is applied to endoscopy videos. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research Paper** & **Purpose** & **Method** & **Database** & **Result** & **Limitations** & **Advantages** \\ \hline \multirow{4}{*}{[PERSON] et al., 2018 [52]} & Classification of 37 gastric diseases and retrieval of similar frames & ResNet with LSTM (both spatial and temporal features are extracted) &
\begin{tabular}{c} GastroLab [31] \\ (publicly available) \\ \end{tabular} & & & & \\  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the contributions where the classification task is applied to endoscopy videos.