Progressive Multi-Scale Frequency-Aware Cholecystectomy Surgery Segmentation based on YOLO
Shriram G
, Yamini Veeramani , Subhasree.A , Amrithalakshmi.T.M
Fatality, SVM, KNN, Ada Boost, Cat Boost
Automated recognition of surgical phases is a prerequisite for computer-assisted analysis of surgeries. The research on phase recognition has been mostly driven by publicly available datasets of laparoscopic cholecystectomy (Lap Chole) videos. Yet, videos observed in real-world settings might contain challenges, such as additional phases and images, which may be missing in curated public datasets.In recent times, many studies concerning surgical video analysis are being conducted due to its growing importance in many medical applications. In particular, it is very important to be able to recognize the current surgical phase because the phase information can be utilized in various ways both during and after surgery.We observed strong differences between our dataset and the most commonly used public dataset for surgical phase recognition, Cholec80. We further trained and compared several state-of-the-art phase recognition models on our dataset. The models performances greatly varied across surgical phases and images. In particular, our results highlighted the challenge of recognizing extremely under-represented phases (usually missing in public datasets); the major phases were recognized with at least higher percent recall. Overall, our results highlighted the need to better understand the distribution of the video data phase recognition models are trained on
"Progressive Multi-Scale Frequency-Aware Cholecystectomy Surgery Segmentation based on YOLO", JNRID - JOURNAL OF NOVEL RESEARCH AND INNOVATIVE DEVELOPMENT (www.JNRID.org), ISSN:2984-8687, Vol.2, Issue 5, page no.a93-a98, May-2024, Available :https://tijer.org/JNRID/papers/JNRID2405007.pdf
Volume 2
Issue 5,
May-2024
Pages : a93-a98
Paper Reg. ID: JNRID_700257
Published Paper Id: JNRID2405007
Downloads: 000266
Research Area: Science and Technology
Country: RAMAPURAM, TAMIL NADU, India
ISSN: 2984-8687 | IMPACT FACTOR: 9.57 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: JNRID (IJ Publication) Janvi Wave
