Image And Pixel Based Scheme For Bleeding Detection In Wireless Capsule Endoscopy Images

Vani, V.; Prashanth, K. V. Mahendra

doi:10.1007/978-3-319-47952-1_13

Cited by 4 publications

(2 citation statements)

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“…However, two different modalities are used to examine the abnormal condition of the GI tract. The first method is traditional endoscopy in which a long, thin tube is inserted into the GI tract to closely examine a damaged internal organ or affected tissue and this procedure is painful for patients [ 12 , 14 , 17 ]. The second method is WCE in which a tiny capsule, about the size of a big vitamin tablet, is swallowed during a capsule endoscopy operation.…”

Section: The Proposed Gi-detection Modelmentioning

confidence: 99%

“…It can be laborious and time-consuming for the doctors to manually diagnose each image in a scenario where the percentage of WCE images with anomalies is just 5–7% of the total WCE images gathered [ 7 , 8 ]. Consequently, a method for automated computer-aided anomaly detection to help clinicians diagnose gastric ulcers is needed [ 12 ]. Numerous studies have been undertaken in the past on the topic of GI tract ulcer identification for endoscopic pictures.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images

et al. 2022

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Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics.

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Section: The Proposed Gi-detection Modelmentioning

confidence: 99%