EFAG-CNN: Effectively fused attention guided convolutional neural network for WCE image classification

Yao, Jiafeng; Zhang, Zhibo; Cheng, Shan; Li, Sheng; Zhu, Jinhui; He, Xiongxiong; Jiang, Qianru

doi:10.1109/ddcls52934.2021.9455575

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…Branch1 has been used to concentrate the lesion region, branch 2 has been used to extract useful features from the lesion area, and branch 3 has been used to combine global and local features to get the final prediction result. 4 When the available labeled datasets are insufficient to produce a supervised model with improved performance, weakly supervision 1,28 is seeking to gather more labeled data for supervised training and modeling. The labeled data that is accessible is noisy or comes from an unreliable source.…”

Section: Deep Learningmentioning

confidence: 99%

“…Another drawback is the unclear line between lesions and normal tissues. 4 Deep learning techniques offer a lot of promise for helping doctors detect, find, and diagnose gastrointestinal disease with wireless capsule endoscopy. Several researchers have created image processing [5][6][7] and deep learning methods for finding and diagnosing disease from gastrointestinal tract problems using wireless capsule endoscopes over the last ten years.…”

Section: Introductionmentioning

confidence: 99%

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Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Habe,

Haataja,

Toivanen

2024

F1000Res

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Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process.

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Section: Deep Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Habe,

Haataja,

Toivanen

2024

F1000Res

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“…It employed the concepts of deep saliency detection and iterative cluster unification for precise detection and localisation. Cao et al proposed an attention-guided network for the classification of WCE images [ 27 ]. Global and local features were extracted from the input images and refined using the attention feature fusion module.…”

Section: Related Workmentioning

confidence: 99%

A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images

et al. 2022

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The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner’s expertise to identify inflammatory regions on the inner surface of the gastrointestinal tract. The length of the alimentary canal and the large volume of images obtained from endoscopic procedures make traditional detection methods time consuming and laborious. Recently, deep learning architectures have achieved better results in the classification of endoscopy images. However, visual similarities between different portions of the gastrointestinal tract pose a challenge for effective disease detection. This work proposes a novel system for the classification of endoscopy images by focusing on feature mining through convolutional neural networks (CNN). The model presented is built by combining a state-of-the-art architecture (i.e., EfficientNet B0) with a custom-built CNN architecture named Effimix. The proposed Effimix model employs a combination of squeeze and excitation layers and self-normalising activation layers for precise classification of gastrointestinal diseases. Experimental observations on the HyperKvasir dataset confirm the effectiveness of the proposed architecture for the classification of endoscopy images. The proposed model yields an accuracy of 97.99%, with an F1 score, precision, and recall of 97%, 97%, and 98%, respectively, which is significantly higher compared to the existing works.

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“…The AASAN proposed is different from the work in [32]. Our previous work EFAG‐CNN is not able to effectively extract global features [33], and it has been proved that features in the neighbourhood of lesions can also contribute to image classification [6]. We aim to imitate the diagnosis process of endoscopists and implement the adaptive selection discriminant feature in the fusion branch.…”

Section: Related Workmentioning

confidence: 99%

Adaptive aggregation with self‐attention network for gastrointestinal image classification

Yao

Zhu

et al. 2022

IET Image Processing

Self Cite

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Automatic classification of diseases in endoscopic images is essential to the improvement of diagnostic performance and the reduction of colorectal cancer mortality. However, due to the ambiguous boundary between background and foreground, abnormal classification in endoscopic images is still challenging. To tackle such a situation, an adaptive aggregation with self‐attention network (AASAN), including a global branch, a local branch, and a fusion branch, is proposed imitating the diagnosis process of endoscopists. On this basis, the self‐attention with relative position encoding (SA‐RPE) module is designed to capture long‐range dependencies and gather lesion neighborhood information. Furthermore, an adaptive aggregation feature (AAF) module is proposed and embedded into the fusion branch for final image label prediction, which is helpful to capture more discriminant features. Extensive experiments show that the classification accuracy of the authors' method on Kvasir public dataset reaches 96.37% in a fivefold cross‐validation, higher than the state‐of‐the‐art deep learning algorithms.

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EFAG-CNN: Effectively fused attention guided convolutional neural network for WCE image classification

Cited by 4 publications

References 13 publications

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images

Adaptive aggregation with self‐attention network for gastrointestinal image classification

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