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Alsubai S. Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques. PeerJ Comput Sci 2024; 10:e1996. [PMID: 38660170 PMCID: PMC11042027 DOI: 10.7717/peerj-cs.1996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/27/2024] [Indexed: 04/26/2024]
Abstract
Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.
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Affiliation(s)
- Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
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2
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Zhang Y, Yang G, Gong C, Zhang J, Wang S, Wang Y. Polyp segmentation with interference filtering and dynamic uncertainty mining. Phys Med Biol 2024; 69:075016. [PMID: 38382099 DOI: 10.1088/1361-6560/ad2b94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/21/2024] [Indexed: 02/23/2024]
Abstract
Objective.Accurate polyp segmentation from colo-noscopy images plays a crucial role in the early diagnosis and treatment of colorectal cancer. However, existing polyp segmentation methods are inevitably affected by various image noises, such as reflections, motion blur, and feces, which significantly affect the performance and generalization of the model. In addition, coupled with ambiguous boundaries between polyps and surrounding tissue, i.e. small inter-class differences, accurate polyp segmentation remains a challenging problem.Approach.To address these issues, we propose a novel two-stage polyp segmentation method that leverages a preprocessing sub-network (Pre-Net) and a dynamic uncertainty mining network (DUMNet) to improve the accuracy of polyp segmentation. Pre-Net identifies and filters out interference regions before feeding the colonoscopy images to the polyp segmentation network DUMNet. Considering the confusing polyp boundaries, DUMNet employs the uncertainty mining module (UMM) to dynamically focus on foreground, background, and uncertain regions based on different pixel confidences. UMM helps to mine and enhance more detailed context, leading to coarse-to-fine polyp segmentation and precise localization of polyp regions.Main results.We conduct experiments on five popular polyp segmentation benchmarks: ETIS, CVC-ClinicDB, CVC-ColonDB, EndoScene, and Kvasir. Our method achieves state-of-the-art performance. Furthermore, the proposed Pre-Net has strong portability and can improve the accuracy of existing polyp segmentation models.Significance.The proposed method improves polyp segmentation performance by eliminating interference and mining uncertain regions. This aids doctors in making precise and reduces the risk of colorectal cancer. Our code will be released athttps://github.com/zyh5119232/DUMNet.
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Affiliation(s)
- Yunhua Zhang
- Northeastern University, Shenyang 110819, People's Republic of China
- DUT Artificial Intelligence Institute, Dalian 116024, People's Republic of China
| | - Gang Yang
- Northeastern University, Shenyang 110819, People's Republic of China
| | - Congjin Gong
- Northeastern University, Shenyang 110819, People's Republic of China
| | - Jianhao Zhang
- Northeastern University, Shenyang 110819, People's Republic of China
| | - Shuo Wang
- Northeastern University, Shenyang 110819, People's Republic of China
| | - Yutao Wang
- Northeastern University, Shenyang 110819, People's Republic of China
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Houwen BBSL, Nass KJ, Vleugels JLA, Fockens P, Hazewinkel Y, Dekker E. Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability. Gastrointest Endosc 2023; 97:184-199.e16. [PMID: 36084720 DOI: 10.1016/j.gie.2022.08.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/24/2022] [Accepted: 08/30/2022] [Indexed: 01/28/2023]
Abstract
BACKGROUND AND AIMS Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) research. Currently, little is known regarding the available number and content of these databases. This review aimed to describe the availability, accessibility, and usability of publicly available colonoscopic imaging databases, focusing on polyp detection, polyp characterization, and quality of colonoscopy. METHODS A systematic literature search was performed in MEDLINE and Embase to identify AI studies describing publicly available colonoscopic imaging databases published after 2010. Second, a targeted search using Google's Dataset Search, Google Search, GitHub, and Figshare was done to identify databases directly. Databases were included if they contained data about polyp detection, polyp characterization, or quality of colonoscopy. To assess accessibility of databases, the following categories were defined: open access, open access with barriers, and regulated access. To assess the potential usability of the included databases, essential details of each database were extracted using a checklist derived from the Checklist for Artificial Intelligence in Medical Imaging. RESULTS We identified 22 databases with open access, 3 databases with open access with barriers, and 15 databases with regulated access. The 22 open access databases contained 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization, and/or segmentation; 6 on polyp characterization, and 3 on quality of colonoscopy. Only half of these databases have been used by other researcher to develop, train, or benchmark their AI system. Although technical details were in general well reported, important details such as polyp and patient demographics and the annotation process were under-reported in almost all databases. CONCLUSIONS This review provides greater insight on public availability of colonoscopic imaging databases for AI research. Incomplete reporting of important details limits the ability of researchers to assess the usability of current databases.
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Affiliation(s)
- Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Karlijn J Nass
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Jasper L A Vleugels
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Paul Fockens
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Yark Hazewinkel
- Department of Gastroenterology and Hepatology, Radboud University Nijmegen Medical Center, Radboud University of Nijmegen, Nijmegen, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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4
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Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement. Comput Biol Med 2022; 147:105760. [DOI: 10.1016/j.compbiomed.2022.105760] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/02/2022] [Accepted: 06/18/2022] [Indexed: 11/19/2022]
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5
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Tang CP, Hsieh CH, Lin TL. Computer-Aided Image Enhanced Endoscopy Automated System to Boost Polyp and Adenoma Detection Accuracy. Diagnostics (Basel) 2022; 12:diagnostics12040968. [PMID: 35454016 PMCID: PMC9025080 DOI: 10.3390/diagnostics12040968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/03/2022] [Accepted: 04/08/2022] [Indexed: 01/04/2023] Open
Abstract
Colonoscopy is the gold standard to detect colon polyps prematurely. Early detection, characterization and resection of polyps decrease colon cancer incidence. Colon polyp missing rate remains high despite novel methods development. Narrowed-band imaging (NBI) is one of the image enhance techniques used to boost polyp detection and characterization, which uses special filters to enhance the contrast of the mucosa surface and vascular pattern of the polyp. However, the single-button-activated system is not convenient for a full-time colonoscopy operation. We selected three methods to simulate the NBI system: Color Transfer with Mean Shift (CTMS), Multi-scale Retinex with Color Restoration (MSRCR), and Gamma and Sigmoid Conversions (GSC). The results show that the classification accuracy using the original images is the lowest. All color transfer methods outperform the original images approach. Our results verified that the color transfer has a positive impact on the polyp identification and classification task. Combined analysis results of the mAP and the accuracy show an excellent performance of the MSRCR method.
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Affiliation(s)
- Chia-Pei Tang
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi City 62224, Taiwan;
- School of Medicine, Tzu Chi University, Hualien City 97004, Taiwan
| | - Chen-Hung Hsieh
- Department of Management Information System, National Chiayi University, Chiayi City 600023, Taiwan;
| | - Tu-Liang Lin
- Department of Management Information System, National Chiayi University, Chiayi City 600023, Taiwan;
- Correspondence:
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6
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Nisha JS, Gopi VP, Palanisamy P. CLASSIFICATION OF INFORMATIVE FRAMES IN COLONOSCOPY VIDEO BASED ON IMAGE ENHANCEMENT AND PHOG FEATURE EXTRACTION. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2022. [DOI: 10.4015/s1016237222500156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Colonoscopy allows doctors to check the abnormalities in the intestinal tract without any surgical operations. The major problem in the Computer-Aided Diagnosis (CAD) of colonoscopy images is the low illumination condition of the images. This study aims to provide an image enhancement method and feature extraction and classification techniques for detecting polyps in colonoscopy images. We propose a novel image enhancement method with a Pyramid Histogram of Oriented Gradients (PHOG) feature extractor to detect polyps in the colonoscopy images. The approach is evaluated across different classifiers, such as Multi-Layer Perceptron (MLP), Adaboost, Support Vector Machine (SVM), and Random Forest. The proposed method has been trained using the publicly available databases CVC ClinicDB and tested in ETIS Larib and CVC ColonDB. The proposed approach outperformed the existing state-of-the-art methods on both databases. The reliability of the classifiers’ performance was examined by comparing their F1 score, precision, F2 score, recall, and accuracy. PHOG with Random Forest classifier outperformed the existing methods in terms of recall of 97.95%, precision 98.46%, F1 score 98.20%, F2 score of 98.00%, and accuracy of 98.21% in the CVC-ColonDB. In the ETIS-LARIB dataset it attained a recall value of 96.83%, precision 98.65%, F1 score 97.73%, F2 score 98.59%, and accuracy of 97.75%. We observed that the proposed image enhancement method with PHOG feature extraction and the Random Forest classifier will help doctors to evaluate and analyze anomalies from colonoscopy data and make decisions quickly.
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Affiliation(s)
- J. S. Nisha
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu 620015, India
| | - Varun P. Gopi
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu 620015, India
| | - P. Palanisamy
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu 620015, India
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7
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Nisha J, P. Gopi V, Palanisamy P. Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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8
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Su H, Lin B, Huang X, Li J, Jiang K, Duan X. MBFFNet: Multi-Branch Feature Fusion Network for Colonoscopy. Front Bioeng Biotechnol 2021; 9:696251. [PMID: 34336808 PMCID: PMC8317500 DOI: 10.3389/fbioe.2021.696251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 05/25/2021] [Indexed: 01/02/2023] Open
Abstract
Colonoscopy is currently one of the main methods for the detection of rectal polyps, rectal cancer, and other diseases. With the rapid development of computer vision, deep learning-based semantic segmentation methods can be applied to the detection of medical lesions. However, it is challenging for current methods to detect polyps with high accuracy and real-time performance. To solve this problem, we propose a multi-branch feature fusion network (MBFFNet), which is an accurate real-time segmentation method for detecting colonoscopy. First, we use UNet as the basis of our model architecture and adopt stepwise sampling with channel multiplication to integrate features, which decreases the number of flops caused by stacking channels in UNet. Second, to improve model accuracy, we extract features from multiple layers and resize feature maps to the same size in different ways, such as up-sampling and pooling, to supplement information lost in multiplication-based up-sampling. Based on mIOU and Dice loss with cross entropy (CE), we conduct experiments in both CPU and GPU environments to verify the effectiveness of our model. The experimental results show that our proposed MBFFNet is superior to the selected baselines in terms of accuracy, model size, and flops. mIOU, F score, and Dice loss with CE reached 0.8952, 0.9450, and 0.1602, respectively, which were better than those of UNet, UNet++, and other networks. Compared with UNet, the flop count decreased by 73.2%, and the number of participants also decreased. The actual segmentation effect of MBFFNet is only lower than that of PraNet, the number of parameters is 78.27% of that of PraNet, and the flop count is 0.23% that of PraNet. In addition, experiments on other types of medical tasks show that MBFFNet has good potential for general application in medical image segmentation.
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Affiliation(s)
- Houcheng Su
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
| | - Bin Lin
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
| | - Xiaoshuang Huang
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
| | - Jiao Li
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
| | - Kailin Jiang
- College of Science, Sichuan Agricultural University, Ya’an, China
| | - Xuliang Duan
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
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9
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Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10481-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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10
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Vedaei SS, Wahid KA. A localization method for wireless capsule endoscopy using side wall cameras and IMU sensor. Sci Rep 2021; 11:11204. [PMID: 34045554 PMCID: PMC8160358 DOI: 10.1038/s41598-021-90523-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 05/11/2021] [Indexed: 02/08/2023] Open
Abstract
Localizing the endoscopy capsule inside gastrointestinal (GI) system provides key information which leads to GI abnormality tracking and precision medical delivery. In this paper, we have proposed a new method to localize the capsule inside human GI track. We propose to equip the capsule with four side wall cameras and an Inertial Measurement Unit (IMU), that consists of 9 Degree-Of-Freedom (DOF) including a gyroscope, an accelerometer and a magnetometer to monitor the capsule’s orientation and direction of travel. The low resolution mono-chromatic cameras, installed along the wide wall, are responsible to measure the actual capsule movement, not the involuntary motion of the small intestine. Finally, a fusion algorithm is used to combine all data to derive the traveled path and plot the trajectory. Compared to other methods, the presented system is resistive to surrounding conditions, such as GI nonhomogeneous structure and involuntary small bowel movements. In addition, it does not require external antenna or arrays. Therefore, GI tracking can be achieved without disturbing patients’ daily activities.
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Affiliation(s)
- Seyed Shahim Vedaei
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada.
| | - Khan A Wahid
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
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11
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Naz J, Sharif M, Yasmin M, Raza M, Khan MA. Detection and Classification of Gastrointestinal Diseases using Machine Learning. Curr Med Imaging 2021; 17:479-490. [PMID: 32988355 DOI: 10.2174/1573405616666200928144626] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 07/07/2020] [Accepted: 07/23/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possible for an expert physician because it's a time taking task to analyze thousands of images thoroughly. Hence, there comes the need for a Computer-Aided-Diagnosis (CAD) method to help doctors analyze images. Many researchers have proposed techniques for automated recognition and classification of abnormality in captured images. METHODS In this article, existing methods for automated classification, segmentation and detection of several GI diseases are discussed. Paper gives a comprehensive detail about these state-of-theart methods. Furthermore, literature is divided into several subsections based on preprocessing techniques, segmentation techniques, handcrafted features based techniques and deep learning based techniques. Finally, issues, challenges and limitations are also undertaken. RESULTS A comparative analysis of different approaches for the detection and classification of GI infections. CONCLUSION This comprehensive review article combines information related to a number of GI diseases diagnosis methods at one place. This article will facilitate the researchers to develop new algorithms and approaches for early detection of GI diseases detection with more promising results as compared to the existing ones of literature.
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Affiliation(s)
- Javeria Naz
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
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12
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Cao C, Wang R, Yu Y, zhang H, Yu Y, Sun C. Gastric polyp detection in gastroscopic images using deep neural network. PLoS One 2021; 16:e0250632. [PMID: 33909671 PMCID: PMC8081222 DOI: 10.1371/journal.pone.0250632] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 04/08/2021] [Indexed: 12/26/2022] Open
Abstract
This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps have various sizes. The difficulty of polyp detection is that small polyps are difficult to detect from the background. We propose a feature extraction and fusion module and combine it with the YOLOv3 network to form our network. This method performs better than other methods in the detection of small polyps because it can fuse the semantic information of high-level feature maps with low-level feature maps to help small polyps detection. In this work, we use a dataset of gastric polyps created by ourselves, containing 1433 training images and 508 validation images. We train and validate our network on our dataset. In comparison with other methods of polyps detection, our method has a significant improvement in precision, recall rate, F1, and F2 score. The precision, recall rate, F1 score, and F2 score of our method can achieve 91.6%, 86.2%, 88.8%, and 87.2%.
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Affiliation(s)
- Chanting Cao
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Ruilin Wang
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Yao Yu
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
- * E-mail:
| | - Hui zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ying Yu
- Beijing An Zhen Hospital, Beijing, China
| | - Changyin Sun
- School of Automation, Southeast University, Nanjing, China
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13
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Comparison of deep learning and conventional machine learning methods for classification of colon polyp types. EUROBIOTECH JOURNAL 2021. [DOI: 10.2478/ebtj-2021-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Determination of polyp types requires tissue biopsy during colonoscopy and then histopathological examination of the microscopic images which tremendously time-consuming and costly. The first aim of this study was to design a computer-aided diagnosis system to classify polyp types using colonoscopy images (optical biopsy) without the need for tissue biopsy. For this purpose, two different approaches were designed based on conventional machine learning (ML) and deep learning. Firstly, classification was performed using random forest approach by means of the features obtained from the histogram of gradients descriptor. Secondly, simple convolutional neural networks (CNN) based architecture was built to train with the colonoscopy images containing colon polyps. The performances of these approaches on two (adenoma & serrated vs. hyperplastic) or three (adenoma vs. hyperplastic vs. serrated) category classifications were investigated. Furthermore, the effect of imaging modality on the classification was also examined using white-light and narrow band imaging systems. The performance of these approaches was compared with the results obtained by 3 novice and 4 expert doctors. Two-category classification results showed that conventional ML approach achieved significantly better than the simple CNN based approach did in both narrow band and white-light imaging modalities. The accuracy reached almost 95% for white-light imaging. This performance surpassed the correct classification rate of all 7 doctors. Additionally, the second task (three-category) results indicated that the simple CNN architecture outperformed both conventional ML based approaches and the doctors. This study shows the feasibility of using conventional machine learning or deep learning based approaches in automatic classification of colon types on colonoscopy images.
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Qadir HA, Shin Y, Solhusvik J, Bergsland J, Aabakken L, Balasingham I. Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction. Med Image Anal 2020; 68:101897. [PMID: 33260111 DOI: 10.1016/j.media.2020.101897] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 10/26/2020] [Accepted: 10/28/2020] [Indexed: 12/24/2022]
Abstract
To decrease colon polyp miss-rate during colonoscopy, a real-time detection system with high accuracy is needed. Recently, there have been many efforts to develop models for real-time polyp detection, but work is still required to develop real-time detection algorithms with reliable results. We use single-shot feed-forward fully convolutional neural networks (F-CNN) to develop an accurate real-time polyp detection system. F-CNNs are usually trained on binary masks for object segmentation. We propose the use of 2D Gaussian masks instead of binary masks to enable these models to detect different types of polyps more effectively and efficiently and reduce the number of false positives. The experimental results showed that the proposed 2D Gaussian masks are efficient for detection of flat and small polyps with unclear boundaries between background and polyp parts. The masks make a better training effect to discriminate polyps from the polyp-like false positives. The proposed method achieved state-of-the-art results on two polyp datasets. On the ETIS-LARIB dataset we achieved 86.54% recall, 86.12% precision, and 86.33% F1-score, and on the CVC-ColonDB we achieved 91% recall, 88.35% precision, and F1-score 89.65%.
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Affiliation(s)
- Hemin Ali Qadir
- Intervention Centre, Oslo University Hospital, Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway; OmniVision Technologies Norway AS, Oslo, Norway.
| | - Younghak Shin
- Department of Computer Engineering, Mokpo National University, Mokpo, Korea.
| | | | | | - Lars Aabakken
- Department of Transplantation Medicine, University of Oslo, Oslo, Norway
| | - Ilangko Balasingham
- Intervention Centre, Oslo University Hospital, Oslo, Norway; Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway
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15
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Ramadan H, Lachqar C, Tairi H. Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101945] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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