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

Ramamurthy, Karthik; George, Timothy Thomas; Shah, Yash; Sasidhar, Parasa

doi:10.3390/diagnostics12102316

Cited by 23 publications

(15 citation statements)

References 43 publications

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“…We need to remove its blood vessel information and compare it with the results of RTNet-base. In addition, we introduce the feature fusion method proposed by Ramamurthy et al [ 27 ]. The following experimental results are taken from the average of five experiments.…”

Section: Resultsmentioning

confidence: 99%

“…Especially for the DR lesion segmentation, there is only a single lesion segmentation dataset, and there is no corresponding associated information for the research. Ramamurthy et al [ 27 ] designed a two-model parallel structure. One model extracts the prominent features of lesions, and the other model calibrates the features effectively.…”

Section: Related Workmentioning

confidence: 99%

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MSLF-Net: A Multi-Scale and Multi-Level Feature Fusion Net for Diabetic Retinopathy Segmentation

et al. 2022

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Diabetic Retinopathy (DR) is a diabetic complication that predisposes patients to visual impairments that could lead to blindness. Lesion segmentation using deep learning algorithms is an effective measure to screen and prevent early DR. However, there are several types of DR with varying sizes and high inter-class similarity, making segmentation difficult. In this paper, we propose a supervised segmentation method (MSLF-Net) based on multi-scale–multi-level feature fusion to achieve accurate end-to-end DR lesion segmentation. MSLF-Net builds a Multi-Scale Feature Extraction (MSFE) module to extract multi-scale information and provide more comprehensive features for segmentation. This paper further introduces the Multi-Level Feature Fusion (MLFF) module to improve feature fusion using a cross-layer structure. This structure only fuses low- and high-level features of the same class based on category supervision, avoiding feature contamination. Moreover, this paper produces additional masked images for the dataset and performs image enhancement operations to ensure that the proposed method is trainable and functional on small datasets. The extensive experiments are conducted on public datasets IDRID and e_ophtha. The results showed that our proposed feature enhancement method can perform feature fusion more effectively. Therefore, In the end-to-end DR segmentation neural network model, MSLF Net is superior to other similar models in segmentation, and can effectively improve the DR lesion segmentation performance.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MSLF-Net: A Multi-Scale and Multi-Level Feature Fusion Net for Diabetic Retinopathy Segmentation

et al. 2022

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“…Recent progress in computer technology, in which copious digitized, high-resolution images are available as big data, has helped improve AI performance. State-of-the-art CNN technologies that can analyze endoscopic imagery with a high accuracy have also been reported on [ 56 ]. These technological advances resulted in the development of high-performance CAD systems for endoscopy.…”

Section: Discussionmentioning

confidence: 99%

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Ochiai

Ozawa

Shibata³

et al. 2022

Diagnostics

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Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist’s skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

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“…It is divided into two main regions: the upper GI tract (including the mouth, pharynx, esophagus, stomach) and the lower GI tract (including the small intestine, large intestine, rectum, and anus). The GI tract is lined with a mucous membrane and muscular layers that work together to move food through the tract and break it down for absorption and elimination [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, due to increased screening, early identification, and improved treatment, the incidence of colorectal cancer is decreasing in many industrialized nations. Hence, visual evaluation of organs in the GI system is required by medical practitioners [ 1 ]. The most common types of stomach issues are gastrointestinal (GI) anomalies such as bleeding, pylorus, erosion, ulcers, and polyps, which require considerable medical treatment because stomach abnormalities induce a variety of diseases.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Digestive Abnormalities from WCE Images by Using AttResU-Net Architecture

Lafraxo

Souaidi

Ansari

et al. 2023

Life

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Colorectal cancer is one of the most common malignancies and the leading cause of cancer death worldwide. Wireless capsule endoscopy is currently the most frequent method for detecting precancerous digestive diseases. Thus, precise and early polyps segmentation has significant clinical value in reducing the probability of cancer development. However, the manual examination is a time-consuming and tedious task for doctors. Therefore, scientists have proposed many computational techniques to automatically segment the anomalies from endoscopic images. In this paper, we present an end-to-end 2D attention residual U-Net architecture (AttResU-Net), which concurrently integrates the attention mechanism and residual units into U-Net for further polyp and bleeding segmentation performance enhancement. To reduce outside areas in an input image while emphasizing salient features, AttResU-Net inserts a sequence of attention units among related downsampling and upsampling steps. On the other hand, the residual block propagates information across layers, allowing for the construction of a deeper neural network capable of solving the vanishing gradient issue in each encoder. This improves the channel interdependencies while lowering the computational cost. Multiple publicly available datasets were employed in this work, to evaluate and verify the proposed method. Our highest-performing model was AttResU-Net, on the MICCAI 2017 WCE dataset, which achieved an accuracy of 99.16%, a Dice coefficient of 94.91%, and a Jaccard index of 90.32%. The experiment findings show that the proposed AttResU-Net overcomes its baselines and provides performance comparable to existing polyp segmentation approaches.

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

Cited by 23 publications

References 43 publications

MSLF-Net: A Multi-Scale and Multi-Level Feature Fusion Net for Diabetic Retinopathy Segmentation

MSLF-Net: A Multi-Scale and Multi-Level Feature Fusion Net for Diabetic Retinopathy Segmentation

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Semantic Segmentation of Digestive Abnormalities from WCE Images by Using AttResU-Net Architecture

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