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

The trade-off between speed and precision is a key step in the detection of small polyps in wireless capsule endoscopy (WCE) images. In this paper, we propose a hybrid network of an inception v4 architecture-based single-shot multibox detector (Hyb-SSDNet) to detect small polyp regions in both WCE a...

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Bibliographic Details
Published in:Diagnostics (Basel) 2022-08, Vol.12 (8), p.2030
Main Authors: Souaidi, Meryem, El Ansari, Mohamed
Format: Article
Language:English
Subjects:
Architecture
Classification
Colonoscopy
Datasets
Deep learning
deep transfer learning
Diagnosis
Endoscopy
image augmentation
inception module
Medical research
Methods
Morphology
multi-scale encoding
polyp
Polyps
Polyps (Pathology)
Semantics
Sensors
weighted feature maps fusion
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Summary:The trade-off between speed and precision is a key step in the detection of small polyps in wireless capsule endoscopy (WCE) images. In this paper, we propose a hybrid network of an inception v4 architecture-based single-shot multibox detector (Hyb-SSDNet) to detect small polyp regions in both WCE and colonoscopy frames. Medical privacy concerns are considered the main barriers to WCE image acquisition. To satisfy the object detection requirements, we enlarged the training datasets and investigated deep transfer learning techniques. The Hyb-SSDNet framework adopts inception blocks to alleviate the inherent limitations of the convolution operation to incorporate contextual features and semantic information into deep networks. It consists of four main components: (a) multi-scale encoding of small polyp regions, (b) using the inception v4 backbone to enhance more contextual features in shallow and middle layers, and (c) concatenating weighted features of mid-level feature maps, giving them more importance to highly extract semantic information. Then, the feature map fusion is delivered to the next layer, followed by some downsampling blocks to generate new pyramidal layers. Finally, the feature maps are fed to multibox detectors, consistent with the SSD process-based VGG16 network. The Hyb-SSDNet achieved a 93.29% mean average precision (mAP) and a testing speed of 44.5 FPS on the WCE dataset. This work proves that deep learning has the potential to develop future research in polyp detection and classification tasks.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12082030