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Multi-Scale Hybrid Network for Polyp Detection in Wireless Capsule Endoscopy and Colonoscopy Images.

Meryem Souaidi¹, Mohamed El Ansari^1,2

¹LABSIV, Computer Science, Faculty of Sciences, University Ibn Zohr, Agadir 80000, Morocco.

Diagnostics (Basel, Switzerland)

|August 26, 2022

Summary

A novel deep learning model, Hyb-SSDNet, enhances small polyp detection in wireless capsule endoscopy (WCE) images. This network achieves high precision and speed, paving the way for improved gastrointestinal diagnostics.

Keywords:

deep transfer learning image augmentation inception module multi-scale encoding polyp single-shot multibox detector (SSD)weighted feature maps fusion wireless capsule endoscopy images (WCE)

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Detecting small polyps in wireless capsule endoscopy (WCE) images presents a challenge due to the speed-precision trade-off.
Medical privacy concerns can limit the acquisition of WCE images, necessitating robust detection methods.

Purpose of the Study:

To propose a hybrid network, Hyb-SSDNet, for accurate and efficient detection of small polyps in WCE and colonoscopy frames.
To address limitations in WCE image acquisition by enlarging datasets and employing deep transfer learning.

Main Methods:

Developed Hyb-SSDNet, a hybrid network combining Inception v4 architecture with a single-shot multibox detector (SSD).
Incorporated inception blocks to enhance contextual and semantic information capture.
Utilized multi-scale encoding, weighted feature concatenation, and feature map fusion for improved feature extraction.
Leveraged deep transfer learning and enlarged datasets for training.

Main Results:

Achieved a mean average precision (mAP) of 93.29% on the WCE dataset.
Demonstrated a testing speed of 44.5 frames per second (FPS).

Conclusions:

Deep learning, specifically the Hyb-SSDNet framework, shows significant potential for advancing polyp detection and classification.
The proposed method effectively balances speed and precision in identifying small polyps from endoscopic imaging.