Wireless Capsule Endoscopy Video Summarization using Transfer Learning and Random Forests

Wireless Capsule Endoscopy (WCE) is a diagnostic technique for identifying gastrointestinal diseases and abnormalities. Gastroenterologists face a considerable challenge when reviewing a lengthy video to identify a disease. The solution to this problem is generating an automated video summarization technique that generates the WCE Video summaries. This paper presents a Video Summarization technique that summarizes the WCE video. The proposed method uses transfer learning and a Random Forest classifier. Using a computationally light and pre-trained MobileNetV2 for feature extraction helped deliver results quickly. Managing small datasets and mitigating the overfitting risk was effectively addressed using Random Forest. The Random Forest's hyperparameters are optimized through the use of Bayesian optimization. The approach proposed has achieved an accuracy of 98.75% in disease prediction while significantly reducing the viewing time for the video summary. Furthermore, it has attained an average F-Score of 0.98, demonstrating its efficacy and reliability.

@article{Kaur2023,
  title     = {Wireless Capsule Endoscopy Video Summarization using Transfer Learning and Random Forests},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Parminder Kaur and Rakesh Kumar},
  doi       = {10.14569/IJACSA.2023.0140938},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140938}
}