# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 8 Ultrasound
### Miscellaneous  
[PERSON] et al. [78] suggested a real-time processing architecture for reconstructing ultrasound images augmented with deep learning methods. To achieve this, they implemented WaveFlow, a collection of ultrasound data acquisition and processing tools integrated with TensorFlow. WaveFlow includes ultrasound environments and signal processing operator libraries. However, the system was only tested on five ultrasound videos. To assess the state of skeletal muscle from ultrasound videos in general conditions, [PERSON] and [PERSON] [79] proposed a CNN-based architecture. The two muscles selected for the study were gastrocnemius medialis and soleus, and the performance varied across all 32 participants, with accuracy ranging from 45% to 56.9%. The survey of current imaging and deep learning techniques applied to ultrasound technology was conducted by different researchers [80,81]. Transfer learning, 3D ultrasound data, speckle noise removal, and publicly available standard datasets were identified as the areas that need attention to improve accuracy. The research papers discussed in this section are summarized in Tables 7-9. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Research Paper** & **Purpose** & **Method** & **Database** & **Result** & **Limitations** & **Advantages** \\ \hline [PERSON] et al., 2017 [68] & Classification of quality of fetal ultrasound & CNN (only spatial features are extracted) & 492 videos (not publicly available) & Accuracy = 93\% & Highly processing time & Improved performance compared with manual classification \\ \hline [PERSON] et al., 2019 [70] & Classification of fetal abdomen, head, and femur & VGG16 and MobileNet V2 (only spatial features are extracted) & 60,363 video frames (not publicly available) & Average accuracy = 85\% & Low accuracy & Point-of-gaze tracked for expert \\ \hline [PERSON] et al., 2021 [71] & Classification of breast cancer & 3D CNN, domain-knowledge-guided temporal attention module and channel attention module (both spatial and temporal features are extracted) & 221 videos, 1024 \(\times\) 768 (not publicly available) & Accuracy = 86.3\%, Sensitivity = 97.2\% & All data belong to the same source & Fast training time \\ \hline [PERSON] et al., 2022 [72] & Classification of atypical hepatocellular carcinoma & 3D CNN, SVM (spatial and temporal features are extracted) & 447 videos (not publicly available) & Accuracy = 98.3\%, Sensitivity = 98\% & Limited data, Semiauto- mated & High accuracy \\ \hline [PERSON] et al., 2017 [73] & Segmentation of thyroid nodule & RF and GoogleNet (spatial features only) & Dataset 1 (428 video frames, 560 \(\times\) 360, publicly available) & Accuracy = 98.29\% & Cannot predict finer granularity scores, highly dependent on radiologist & Cost-sensitive random forest classifier \\ [PERSON] et al., 2017 [73] & Segmentation of thyroid nodule & GoogleNet (spatial features only) & 122 images with sizes 1024 \(\times\) 695 & Accuracy = 96\% & Cannot predict finer granularity scores, highly dependent on radiologist & Cost-sensitive random forest classifier \\ \hline  & & CNN, spatial transformers & & & & \\ [PERSON] et al., 2020 [74] & Segmentation of COVID-19 markers & network, and soft & 227 videos (spatial and temporal features) & & Data prone to certain bias (includes only severe cases) & Focus on ultrasound instead of CT scan \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of the contributions where the classification task is applied to ultrasound videos.