2 research outputs found

    Deep learning to find colorectal polyps in colonoscopy: A systematic literature review

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    Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.This work was partially supported by PICCOLO project. This project has received funding from the European Union's Horizon2020 Research and Innovation Programme under grant agreement No. 732111. The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein. The authors would also like to thank Dr. Federico Soria for his support on this manuscript and Dr. José Carlos Marín, from Hospital 12 de Octubre, and Dr. Ángel Calderón and Dr. Francisco Polo, from Hospital de Basurto, for the images in Fig. 4

    Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging

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    A computer-aided diagnostic system for colonoscopic imaging has been developed to classify colorectal polyps by type. The modules of the proposed system include image enhancement, feature extraction, feature selection and polyp classification. Three hundred sixty-five images (214 with hyperplastic polyps and 151 with adenomatous polyps) were collected from a branch of a medical center in central Taiwan. The raw images were enhanced by the principal component transform (PCT). The features of texture analysis, spatial domain and spectral domain were extracted from the first component of the PCT. Sequential forward selection (SFS) and sequential floating forward selection (SFFS) were used to select the input feature vectors for classification. Support vector machines (SVMs) were employed to classify the colorectal polyps by type. The classification performance was measured by the Az values of the Receiver Operating Characteristic curve. For all 180 features used as input vectors, the test data set yielded Az values of 88.7%. The Az value was increased by 2.6% (from 88.7% to 91.3%) and 4.4% (from 88.7% to 93.1%) for the features selected by the SFS and the SFFS, respectively. The SFS and the SFFS reduced the dimension of the input vector by 57.2% and 73.8%, respectively. The SFFS outperformed the SFS in both the reduction of the dimension of the feature vector and the classification performance. When the colonoscopic images were visually inspected by experienced physicians, the accuracy of detecting polyps by types was around 85%. The accuracy of the SFFS with the SVM classifier reached 96%. The classification performance of the proposed system outperformed the conventional visual inspection approach. Therefore, the proposed computer-aided system could be used to improve the quality of colorectal polyp diagnosis. (C) 2014 Published by Elsevier Ltd
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