Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network

Abhishek Srivastava, Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve the anomaly detection rate, reduce physicians' time for screening, and aid in real-world clinical analysis. Computer-Aided diagnosis (CADx) classification system for video capsule endoscopy has shown a great promise for further improvement. For example, detection of cancerous polyp and bleeding can lead to swift medical response and improve the survival rate of the patients. To this end, an automated CADx system must have high throughput and decent accuracy. In this study, we propose FocalConvNet, a focal modulation network integrated with lightweight convolutional layers for the classification of small bowel anatomical landmarks and luminal findings. FocalCon-vNet leverages focal modulation to attain global context and allows global-local spatial interactions throughout the forward pass. Moreover, the convolutional block with its intrinsic induetive/learning bias and capacity to extract hierarchical features allows our FocalConvNet to achieve favourable results with high throughput. We compare our FocalConvNet with other state-of-the-art (SOTA) on Kvasir-Capsule, a large-scale VCE dataset with 44,228 frames with 13 classes of different anomalies. We achieved the weighted F1-score, recall and Matthews correlation coefficient (MCC) of 0.6734, 0.6373 and 0.2974, respectively, outperforming SOTA methodologies. Further, we obtained the highest throughput of 148.02 images/second rate to establish the potential of FocalConvNet in a real-time clinical environ-ment. The code of the proposed FocalConvNet is available at https://github.com/NoviceMAn-prog/FocalConvNet.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 35th International Symposium on Computer-Based Medical Systems, CBMS 2022
EditorsLinlin Shen, Alejandro Rodriguez Gonzalez, KC Santosh, Zhihui Lai, Rosa Sicilia, Joao Rafael Almeida, Bridget Kane
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages323-328
Number of pages6
ISBN (Electronic)9781665467704
DOIs
StatePublished - 2022
Event35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022 - Shenzhen, China
Duration: Jul 21 2022Jul 23 2022

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2022-July
ISSN (Print)1063-7125

Conference

Conference35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022
Country/TerritoryChina
CityShenzhen
Period7/21/227/23/22

Keywords

  • Kvasir-Capsule
  • Video capsule endoscopy
  • deep learning
  • gastrointestinal image classification
  • small intestine

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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