Automated Diagnosis of Barrett’s Esophagus with Endoscopic Images

Rajan, Purnima; Canto, Marcia Irene; Gorospe, Emmanuel C.; Almario, A.; Kage, Andreas; Winter, Christian; Hager, Gregory D.; Wittenberg, Thomas; Münzenmayer, Christian

doi:10.1007/978-3-642-03882-2_581

Cited by 8 publications

(4 citation statements)

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“…Rajan et al [33] performed a comparison experiment using several classifiers, such as SVM, k-NN, and Boosting on images from different endoscopy modalities (WLE, NBI, Chromoendoscopy). The datasets (125 WLE images, 122 NBI images, and 150 Chromoendoscopy images) have been classified between four categories: Normal Squamous, Gastric Mucosa, BE, and High-grade dysplasia (adenocarcinoma).…”

Section: Comparison Among Classifiers For Barrett's Esophagus Recognimentioning

confidence: 99%

A survey on Barrett's esophagus analysis using machine learning

Souza

Palm

Mendel

et al. 2018

Computers in Biology and Medicine

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This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.

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Section: Comparison Among Classifiers For Barrett's Esophagus Recognimentioning

confidence: 99%

A survey on Barrett's esophagus analysis using machine learning

Souza

Palm

Mendel

et al. 2018

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…The novelty of their approach lies in the interactive loop provided by a relevance feedback algorithm to improve detection accuracy. [112], presented a comparative evaluation of SVM, K-NN and boosting for detection of OA under NBI, WL and chromoendoscopy. [113] propose to train an SVM classifier using local colour and texture features, from on the original and on the Gabor-filtered image.…”

Section: Endoscopic Applicationsmentioning

confidence: 99%

Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy

Vemuri

2019

Preprint

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“…18 To benefit from the CLE capabilities, the physicians need to be very well trained to have the ability to differentiate between the small changes of the different pathology stages. 19 The main aim of this study is to develop a system that can automatically and accurately classify the IM (precancerous) and NPL (cancerous) stages from the other types of cell deformation in the esophagus tube as they are considered important stages in the detection of BE. The model also can serve as a second opinion for physicians and will support a decrease in the number of biopsy samples needed for each patient.…”

Section: Introductionmentioning

confidence: 99%

In-vivo Barrett’s esophagus digital pathology stage classification through feature enhancement of confocal laser endomicroscopy

et al. 2019

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Barrett's esophagus (BE) is a premalignant condition that has an increased risk to turn into esophageal adenocarcinoma. Classification and staging of the different changes (BE in particular) in the esophageal mucosa are challenging since they have a very similar appearance. Confocal laser endomicroscopy (CLE) is one of the newest endoscopy tools that is commonly used to identify the pathology type of the suspected area of the esophageal mucosa. However, it requires a well-trained physician to classify the image obtained from CLE. An automatic stage classification of esophageal mucosa is presented. The proposed model enhances the internal features of CLE images using an image filter that combines fractional integration with differentiation. Various features are then extracted on a multiscale level, to classify the mucosal tissue into one of its four types: normal squamous (NS), gastric metaplasia (GM), intestinal metaplasia (IM or BE), and neoplasia. These sets of features are used to train two conventional classifiers: support vector machine (SVM) and random forest. The proposed method was evaluated on a dataset of 96 patients with 557 images of different histopathology types. The SVM classifier achieved the best performance with 96.05% accuracy based on a leave-one-patient-out cross-validation. Additionally, the dataset was divided into 60% training and 40% testing; the model achieved an accuracy of 93.72% for the testing data using the SVM. The presented model showed superior performance when compared with four state-of-the-art methods. Accurate classification is essential for the intestinal metaplasia grade, which most likely develops into esophageal cancer. Not only does our method come to the aid of physicians for more accurate diagnosis by acting as a second opinion, but it also acts as a training method for junior physicians who need practice in using CLE. Consequently, this work contributes to an automatic classification that facilitates early intervention and decreases samples of required biopsy.

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Automated Diagnosis of Barrett’s Esophagus with Endoscopic Images

Cited by 8 publications

References 0 publications

A survey on Barrett's esophagus analysis using machine learning

A survey on Barrett's esophagus analysis using machine learning

Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy

In-vivo Barrett’s esophagus digital pathology stage classification through feature enhancement of confocal laser endomicroscopy

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