Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model

Yogapriya, J.; Sumithra, M. G.; Anitha, P.; Jenopaul, P.; Dhas, C. Suresh Gnana

doi:10.1155/2021/5940433

Cited by 51 publications

(38 citation statements)

References 33 publications

(32 reference statements)

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“…Researchers [ 39 ] utilized 12,147 stomach disease endoscopic images to extract from Kvasir v2 and endoscopy artifact detection (EAD) [ 40 ] for disease recognition, using a deep-learning-based attenuation technique and achieved a classification accuracy of 93.19%. In [ 41 ] stomach disease recognition was performed on 6702 images from Kvasir V2, challenging the stomach disease dataset using data augmentation and fine-tuning of CNN models for stomach disease classification with an accuracy of 96.33%. In [ 42 ] 2006, capsule endoscopy images acquired from Kiang Wu Hospital used by researchers for the classification of gastric disease using a deep attention model for segmentation to locate the lesion region for accurate recognition of disease recognition and attained 96.76% of stomach disease classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification

Mohammad

Al-Razgan

2022

Sensors

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Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.

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Section: Discussionmentioning

confidence: 99%

Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification

Mohammad

Al-Razgan

2022

Sensors

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“…Georgakopoulos et al 17 employed a CNN to detect inflammatory gastrointestinal lesions. Some researchers [18][19][20] applied a variety of CNN models, such as VGG16, Inception V3, ResNet-18, GoogLeNet, ResNet50, and ResNet101, pretrained on the ImageNet data set to classify endoscopic images. Hirasawa et al 21 built a CNN model to detect gastric cancer in endoscopic images.…”

Section: Gastrointestinal Endoscopic Image Classificationmentioning

confidence: 99%

Convolutional‐capsule network for gastrointestinal endoscopy image classification

Wang

Yang

et al. 2022

Int J of Intelligent Sys

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Automated diagnosis of digestive tract diseases from gastrointestinal endoscopy images is of high importance for improving the diagnosis accuracy and efficiency. The current mainstream methods for image classification of digestive tract endoscopy images are based on Convolutional Neural Networks (CNNs). However, due to their inherent defects, CNNs are not strong enough in learning deformation‐invariant global features which is essential in gastrointestinal endoscopic image classification. To solve this problem, in this paper we present a two‐stage endoscopic image classification method which can effectively combine complementary advantages of midlevel CNN features and a capsule network. Specifically, the core of our method is a lesion‐aware CNN feature extraction module which can encode sufficiently detailed information of lesions in midlevel CNN features and in turn enable the subsequent capsule classification network to effectively learn deformation‐invariant relationships between image entities. Extensive experiments demonstrate the superiority of our method to the state‐of‐the‐art methods with the classification accuracy of 94.83% on the Kvasir v2 data set and the classification accuracy of 85.99% on the HyperKvasir data set.

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“…Many researchers from all around the world have created a variety of ML models [ 9 ] that have shown notable proficiency in carrying out such automated activities, and they can develop and improve biomedical image analysis. However, Convolutional Neural Network (CNN)-based deep-learning techniques have made significant strides in classification problems recently [ 10 ]. This collection of DL models introduces a hybrid CNN and CNN model with histogram stretching [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…CNN is a system of Input, output, and several hidden convolutional layers that serve as intermediary layers in CNN design. Moreover, it also includes dense layers for training parameters based on convolutional layers, and flattened layers to convert multi-dimension inputs into one-dimensional output [ 9 , 10 ]. These layers carry out operations on the data to discover characteristics unique to the data.…”

Section: Introductionmentioning

confidence: 99%

“…These layers carry out operations on the data to discover characteristics unique to the data. The neurons in each layer of the neural network calculate the weighted average of their input, and this average is then passed through a non-linear function known as an activation function [ 10 , 11 ]. DL provides a variety of activation functions such as Rectified Linear Unit (ReLU), and SoftMax is a fundamentally essential part of CNN.…”

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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Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model

Cited by 51 publications

References 33 publications

Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification

Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification

Convolutional‐capsule network for gastrointestinal endoscopy image classification

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

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