Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy videos

Charfi, Said; Ansari, Mohamed El

doi:10.1109/atsip.2017.8075590

Cited by 19 publications

(17 citation statements)

References 23 publications

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“…Scholars widely use machine learning and deep learning techniques to develop models for detecting stomach diseases. The authors in [ 17 ] developed a system to detect an ulcer using two descriptors, the complete local binary pattern (CLBP) and the global local oriented edge magnitude pattern (Global LOEMP), to obtain texture features and color features. The accuracy of the proposed method (94.07% using SVM) was high compared with the other existing methods at that time.…”

Section: Related Workmentioning

confidence: 99%

“…However, due to the resemblance of different symptoms including color, shape, texture, etc., it is challenging to accurately classify the type of infection. Most of the previous work was conducted on the detection of a single disease/infection [ 1 , 11 , 16 , 17 , 18 ]. Accurate classification of four significant diseases (gastritis, esophagitis, peptic ulcers, and bleeding) and healthy images using a single framework is still challenging.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos

et al. 2021

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In medical imaging, the detection and classification of stomach diseases are challenging due to the resemblance of different symptoms, image contrast, and complex background. Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid method to detect and classify stomach diseases using endoscopy videos. The proposed methodology comprises seven significant steps: data acquisition, preprocessing of data, transfer learning of deep models, feature extraction, feature selection, hybridization, and classification. We selected two different CNN models (VGG19 and Alexnet) to extract features. We applied transfer learning techniques before using them as feature extractors. We used a genetic algorithm (GA) in feature selection, due to its adaptive nature. We fused selected features of both models using a serial-based approach. Finally, the best features were provided to multiple machine learning classifiers for detection and classification. The proposed approach was evaluated on a personally collected dataset of five classes, including gastritis, ulcer, esophagitis, bleeding, and healthy. We observed that the proposed technique performed superbly on Cubic SVM with 99.8% accuracy. For the authenticity of the proposed technique, we considered these statistical measures: classification accuracy, recall, precision, False Negative Rate (FNR), Area Under the Curve (AUC), and time. In addition, we provided a fair state-of-the-art comparison of our proposed technique with existing techniques that proves its worthiness.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos

et al. 2021

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“…In [44], a multi-scale approach based on LBP and Laplacian pyramid transform was proposed for ulcer detection. The last category is for methods based on both colour and texture [9,11,48,52]. In [11], aiming at boosting the chromatic features of ulcerous WCE images, the former ones were color rotated.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the classification was performed using a new vector supported convex hull algorithm. In a related work, complete LBP and global‐local oriented edge magnitude pattern descriptors were combined to detect texture and colour features of ulcer in WCE images [17]. In a different approach for ulcer detection using WCE images, the geometry features of ulcer were exploited in [21].…”

Section: Related Workmentioning

confidence: 99%

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Charfi

Ansari

Balasingham

2019

IET Image Processing

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Wireless capsule endoscopy (WCE) has revolutionized the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large amount of images to be inspected by the clinicians. Hence, the design of a computer-aided diagnosis (CAD) system will have a great potential to help reducing the diagnosis time and improve the detection accuracy. To address this problem, we propose a CAD system for automatic detection of ulcer in WCE images. Firstly, we enhance the input images to be better exploited in the main steps of the proposed method. Afterwards, segmentation using saliency map based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results.

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“…The fundamental steps of the automated detection systems are features extraction, feature selection, and classification. Different methods for feature extraction utilized by the researchers are include point features [17], texture features [15], HOG features [18], and color features [19,20]. Convolutional Neural Networks (CNNs) are combined with the handcrafted features to enhance the system's performance.…”

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confidence: 99%

Computer-Aided Gastrointestinal Diseases Analysis From Wireless Capsule Endoscopy: A Framework of Best Features Selection

et al. 2020

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The continuous improvements in the area of medical imaging, makes the patient monitoring a crucial concern. The internet of things (IoT) embedded in a medical technologies to collect data from human body through sensors, wireless connectivity etc. The junction of medicine and IT like medical informatics will transform healthcare, curbing cost, make more efficient, and saving lives. Various computerized techniques are implemented in the area of Artificial Intelligence (AI) for the application of medical imaging to diagnose the infected regions in the images and videos such as WCE and pathology. The famous stomach infections are ulcer, polyp, and bleeding. Stomach cancer is the most common infection and a leading cause of human deaths worldwide. In the USA, since 2019, a total of 27,510 new cases are reported including 17,230 men and 10,230 women. While the number of deaths is 11,140 consists of 6,800 men and 4,340 women. The manual diagnosis of these stomach infections is a difficult and agitated process therefore it is required to design a fully automated system using AI. In this article, we presented a fully automated system for stomach infection recognition based on deep learning features fusion and selection. In this design, ulcer images are assigned manually and support to a saliency-based method for ulcer detection. Later, pre-trained deep learning model named VGG16 is employing and retrained using transfer learning. Features of re-trained model are extracted from two consecutive fully connected layers and fused by array-based approach. Besides, the best individuals are selected through the metaheuristic approach name PSO along mean value-based fitness function. The selected individuals are finally recognized through Cubic SVM. The experiments are conducted on Private collected dataset and achieved an accuracy of 98.4%, which is best as compared to existing state-of-the-art techniques.

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Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy videos

Cited by 19 publications

References 23 publications

Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos

Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Computer-Aided Gastrointestinal Diseases Analysis From Wireless Capsule Endoscopy: A Framework of Best Features Selection

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