An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features (original) (raw)
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International Journal of Electrical and Computer Engineering (IJECE), 2020
Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy, but the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ=0 0 , 45 0 , 90 0 , 135 0). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases.
Automated polyp detection system in colonoscopy using deep learning and image processing techniques
2019
Introduction: Undetected colonic polyps are considered a major cause of interval cancer of the colon. The Automatic Polyp Detection System (APDS) (Magentiq Eye LTD, Haifa, Israel) was developed to enhance the ability of endoscopists to detect polyps during screening colonoscopy. It is designed to be used both in real-time and offline. APDS runs directly on the video output of the endoscopic camera and highlights the polyp on the screen. APDS utilizes the power of Deep Learning and Computer Vision in order to improve polyp detection rates thus improving the performance of the endoscopist.
Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations
PLOS ONE
Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to train and evaluate the machine learning models for polyp classification. We have also compared the performance of eight state-of-the-art deep learning-based object detection models. The results demonstrate that deep CNN models are promising in CRC screening. This work can serve as a baseline for future research in polyp detection and classification.
Polyp Detection in Endoscopic Video Using SVMs
2007
Colon cancer is one of the most common cancers in developed countries. Most of these cancers start with a polyp. Polyps are easily detected by physicians. Our goal is to mimic this detection ability so that endoscopic videos can be pre-scanned with our algorithm before the physician analyses them. The method will indicate which part of the video needs attention (polyps were detected there) and hence can speedup the procedures. In this paper we present a method for polyp detection in endoscopic images that uses SVM for classification. Our experiments yielded a result of 93.16 ± 0.09% of area under the Receiver Operating Characteristic (ROC) curve on a database of 4620 images indicating that the approach proposed is well suited to the detection of polyps in endoscopic video.
Multimedia Tools and Applications, 2020
In this paper, a computer based system has been proposed as a support to gastrointestinal polyp detection. It can detect and classify gastrointestinal polyps from endoscopic video. Color wavelet (CW) features and convolutional neural network (CNN) features of endoscopic video frames are extracted. Mutual information based feature selection technique-Minimum redundancy maximum relevance (mRMR) is used to scale down feature vector. Instead of using a single classifier, Bootstrap Aggregrating (Bagging)-an ensemble classifier is used. Proposed system has been assessed against different public databases and our own datasets. Evaluation shows that, the system outperforms the existing methods.
Sensors
Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work i...
An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy
Computers in Biology and Medicine, 2006
Today 95% of all gastrointestinal carcinomas are believed to arise from adenomas. The early detection of adenomas could prevent their evolution to cancer. A novel system for the support of the detection of adenomas in gastrointestinal video endoscopy is presented. Unlike other systems, it accepts standard low-resolution video input thus requiring less computational resources and facilitating both portability and the potential to be used in telemedicine applications. It combines intelligent processing techniques of SVMs and color-texture analysis methodologies into a sound pattern recognition framework. Concerning the system's accuracy this was measured using ROC analysis and found to exceed 94%.
Polyp Classification Using Multiple CNN-SVM Classifiers from Endoscope Images
This paper proposes a classification approach of a malignant or bening polyp type by multiple CNN-SVM classifiers using Convolutional Neural Networks (CNN) as feature extractor and Support Vector Machine (SVM) as classifier from three kinds of endoscope images as white light image, dye image and Narrow Band Image (NBI). First, the polyp feature is extracted using CNN as feature extractor from three kinds of endoscope images using each datasets. Second, classifiers are generated as many as three kinds of combinations using SVM and each image is classified. Finally, the final classification result is judged by voting processing from the result obtained by each classifier. The effectiveness of the proposed method was confirmed through experiments in which both validity and accuracy of multiple CNN-SVM voting results were evaluated using actual malignant or benign polyp images.
CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames
Computer Methods and Programs in Biomedicine, 2003
In this paper, we present CoLD (colorectal lesions detector) an innovative detection system to support colorectal cancer diagnosis and detection of pre-cancerous polyps, by processing endoscopy images or video frame sequences acquired during colonoscopy. It utilizes second-order statistical features that are calculated on the wavelet transformation of each image to discriminate amongst regions of normal or abnormal tissue. An artificial neural network performs the classification of the features. CoLD integrates the feature extraction and classification algorithms under a graphical user interface, which allows both novice and expert users to utilize effectively all system's functions. It has been developed in close cooperation with gastroenterology specialists and has been tested on various colonoscopy videos. The detection accuracy of the proposed system has been estimated to be more than 95%. As it has been resulted, it can be used as a supplementary diagnostic tool for colorectal lesions.
Automatic Colorectal Polyp Detection in Colonoscopy Video Frames
Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.