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Journal articles on the topic 'Computer-Aided Diagnosis (CADx)'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Dhull, Anuradha, Kavita Khanna, Akansha Singh, and Gaurav Gupta. "ACO Inspired Computer-aided Detection/Diagnosis (CADe/CADx) Model for Medical Data Classification." Recent Patents on Computer Science 12, no. 4 (2019): 250–59. http://dx.doi.org/10.2174/2213275912666181205155018.

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Background: Computer-Assisted Diagnosis (CAD) has become a common practice of use in the healthcare industry due to its improved accuracy and reliability. The CAD systems are expected to improve the quality of medical care by assisting healthcare professionals with a wide range of clinical decisions. A CAD system is a combination of Computer-Assisted Detection (CADe) and Computer-Assisted Diagnosis (CADx) system. Objective: The objective of this research article is to generate an optimized rule-set for medical diagnosis capable of providing improved accuracy. It is evident from the literature

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Ziyad, Shabana Rasheed, Venkatachalam Radha, and Thavavel Vayyapuri. "Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (2020): 16–26. http://dx.doi.org/10.2174/1573405615666190206153321.

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Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD a

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Parveen, S. Shaik, and Dr C. Kavitha. "A REVIEW ON COMPUTER AIDED DETECTION AND DIAGNOSIS OF LUNG CANCER NODULES." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 3 (2012): 393–400. http://dx.doi.org/10.24297/ijct.v3i3a.2944.

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In this paper, a attempt has been made to summarize some of the information about CAD and CADx for the purpose of early detection and diagnosis of lung cancer. Computer Aided Detection (CADe) and Computer Aided Diagnosis (CADx), are procedures in medical information that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and Ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. Thus, the use of digital computers to assist practitioners and physicians in diagnosing disea

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KURT, Burçin, Vasif V. NABİYEV, and Kemal TURHAN. "AN AUTOMATED COMPUTER-AIDED DETECTION (CADe) AND DIAGNOSIS (CADx) SYSTEM FOR BREAST MICROCALCIFICATIONS IN MAMMOGRAMS." Selcuk University Journal of Engineering ,Science and Technology 6, no. 3 (2018): 355–76. http://dx.doi.org/10.15317/scitech.2018.138.

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Jukema, Jelmer, Koen Kusters, Martijn Jong, et al. "COMPUTER-AIDED DIAGNOSIS (CADX) IMPROVES CHARACTERIZATION OF BARRETT’S NEOPLASIA BY ENDOSCOPISTS." Gastrointestinal Endoscopy 97, no. 6 (2023): AB737—AB738. http://dx.doi.org/10.1016/j.gie.2023.04.1211.

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Choridah, Lina, Nurhuda Hendra Setyawan, Faisal Najamuddin, and Hanung Adi Nugroho. "Computer-Aided Detection (CAD) Berbasis Intensity Correlation Analysis (ICA)." Jurnal Radiologi Indonesia 2, no. 1 (2016): 12–16. http://dx.doi.org/10.33748/jradidn.v2i1.38.

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Objectives: Mammography has an important role in the diagnosis of breast cancer. computer-aided detection (CADe) or computeraided diagnosis (CADx) systems have been developed to improve the capability of radiologists to interpret medical images and to di?erentiate benign and malignant lesions. We studied the capability of ICA as a technique used in CAD systems.Methods: An observational study involving 20 participants (5 radiologists and 15 last year radiology residents) has been initiated. First, they performed assessment of the 60 digitized flm mammograms consisting of 27 normal and 33 abnorm

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Berg, Wendie A., David Gur, Andriy I. Bandos, et al. "Impact of Original and Artificially Improved Artificial Intelligence–based Computer-aided Diagnosis on Breast US Interpretation." Journal of Breast Imaging 3, no. 3 (2021): 301–11. http://dx.doi.org/10.1093/jbi/wbab013.

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Abstract Objective For breast US interpretation, to assess impact of computer-aided diagnosis (CADx) in original mode or with improved sensitivity or specificity. Methods In this IRB approved protocol, orthogonal-paired US images of 319 lesions identified on screening, including 88 (27.6%) cancers (median 7 mm, range 1–34 mm), were reviewed by 9 breast imaging radiologists. Each observer provided BI-RADS assessments (2, 3, 4A, 4B, 4C, 5) before and after CADx in a mode-balanced design: mode 1, original CADx (outputs benign, probably benign, suspicious, or malignant); mode 2, artificially-high-

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Tanmay Ghosh, Khan Alamgir, Yang Xingyao, Muhammad Fayaz,. "Computer-Aided Diagnostic System for Digital Mammography." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 989–95. http://dx.doi.org/10.17762/itii.v9i2.443.

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In this work, Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) systems are developed and tested using the public and freely available mammographic databases named MIAS and DDSM databases, respectively. CADe system is used to differentiate between normal and abnormal tissues, and it assists radiologists to avoid missing a breast abnormality. At the same time, CADx is developed to distinguish between normal, benign and malignant breast tissues, and it helps radiologists to decide whether a biopsy is needed when reading a diagnostic mammogram or not. Any CAD system is constitut

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Wang, Shijun, Karen Burtt, Baris Turkbey, Peter Choyke, and Ronald M. Summers. "Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/789561.

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Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions t

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Oleksiw, M., R. Djinbachian, and D. von Renteln. "A9 IMPLEMENTATION STRATEGIES TO OPTIMIZE DIAGNOSTIC ACCURACY OF COMPUTER-ASSISTED OPTICAL POLYP DIAGNOSIS." Journal of the Canadian Association of Gastroenterology 8, Supplement_1 (2025): i5. https://doi.org/10.1093/jcag/gwae059.009.

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Abstract Background Artificial intelligence (AI) has enabled the development of computer-aided diagnosis (CADx) systems which offer real-time endoscopic pathology prediction of colorectal polyps. However, the clinical benefit of CADx assisted optical diagnosis remains questionable due to lack of diagnostic ability improvement compared to non CADx assisted optical diagnosis. Aims This study aimed to assess diagnostic performance of a novel implementation framework in which optical diagnosis replaces pathology only if CADx and endoscopist agree on polyp diagnosis. We aimed to compare this propos

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Nisworo, Sapto, and Alifia Revan Prananda. "A Review on Digital Microscopic Images for Plasmodium Parasite Detection." TEKNIK 44, no. 3 (2023): 222–29. http://dx.doi.org/10.14710/teknik.v44i3.53990.

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Indonesia is one of the regions that contribute to the increasing number of malaria cases. In 2019, more than 250 million malaria cases were found in Indonesia. This phenomenon is caused by several factors, including the examination procedure. In Indonesia, the digital microscopic examination has become the gold standard procedure in detecting and diagnosing malaria, whereas this procedure requires considerable expertise. Hence, the rapid examination is difficult to ensure. In order to overcome this problem, several methods of malaria detection have been proposed with a different approach. Ima

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Rizkala, T., C. Hassan, M. Spadaccini, et al. "Comparative Optical Diagnosis Performance of Autonomous Computer-Aided Diagnosis (CADx) Versus Human-Assisted by CADx: A Systematic Review and Meta-Analysis." Endoscopy 57, S 02 (2025): S187. https://doi.org/10.1055/s-0045-1805466.

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Sollini, Martina, Margarita Kirienko, Noemi Gozzi, et al. "The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung Lesions on CT Scans: Ready for the “Real World”?" Cancers 15, no. 2 (2023): 357. http://dx.doi.org/10.3390/cancers15020357.

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(1) Background: Once lung lesions are identified on CT scans, they must be characterized by assessing the risk of malignancy. Despite the promising performance of computer-aided systems, some limitations related to the study design and technical issues undermine these tools’ efficiency; an “intelligent agent” to detect and non-invasively characterize lung lesions on CT scans is proposed. (2) Methods: Two main modules tackled the detection of lung nodules on CT scans and the diagnosis of each nodule into benign and malignant categories. Computer-aided detection (CADe) and computer aided-diagnos

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Bhattacharya, Kaushik. "Artificial Intelligence in Colonoscopy." Indian Journal of Colo-Rectal Surgery 7, no. 2 (2024): 25–27. https://doi.org/10.4103/ijcs.ijcs_12_24.

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ABSTRACT Artificial intelligence (AI) is transforming the field of colonoscopy by enhancing diagnostic precision, improving detection rates of polyps, and optimizing procedural efficiency. Recent advances such as computer-aided detection (CADe) and computer-aided diagnosis (CADx) play crucial roles in improving adenoma detection rates (ADRs) and differentiating between benign and malignant polyps in real time. CADe systems alert endoscopists to small or flat lesions often missed in traditional procedures, leading to increased ADR and consistent quality across providers. CADx further supports t

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Karačić, Petra. "COMPUTER-AIDED DIAGNOSIS AND DETECTION IN CHEST RADIOGRAPHY." Zdravstveni glasnik 11, no. 1 (2025): 111–21. https://doi.org/10.47960/2303-8616.2025.1.11.111.

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Artificial intelligence (AI) is a computer science that deals with the ability of computers and robots to perform tasks that require some form of intelligence, such as learning, planning, object recognition, understanding foreign languages, etc. It is applied in various fields such as medicine, science, finance, entertainment, security. Artificial intelligence is slowly but surely being integrated into the healthcare system, where it aims to improve patient outcomes, reduce costs, and increase efficiency. It has a special role in the field of radiology, where it should increase diagnostic and

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Attallah, Omneya, and Maha Sharkas. "GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases." PeerJ Computer Science 7 (March 10, 2021): e423. http://dx.doi.org/10.7717/peerj-cs.423.

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Gastrointestinal (GI) diseases are common illnesses that affect the GI tract. Diagnosing these GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis (CADx) system based on deep learning (DL) techniques could considerably lower the examination cost processes and increase the speed and quality of diagnosis. Therefore, this article proposes a CADx system called Gastro-CADx to classify several GI diseases using DL techniques. Gastro-CADx involves three progressive stages. Initially, four different CNNs are used as feature extractors to extract spatial features. M

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Attallah, Omneya, Fatma Anwar, Nagia M. Ghanem, and Mohamed A. Ismail. "Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images." PeerJ Computer Science 7 (April 27, 2021): e493. http://dx.doi.org/10.7717/peerj-cs.493.

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Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic di

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Saleh, A. Alzahrani, and S. Alqasemi Umar. "Computer Aided Diagnosis of Ventricular Arrhythmias from Electrocardiogram Lead II Signals." Signal & Image Processing : An International Journal (SIPIJ) 9, no. 5 (2019): 1–8. https://doi.org/10.5281/zenodo.3471362.

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In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the

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Dhull, A., and Gaurav Gupta. "An Intelligent Two Phase Fuzzy Decision Tree Based Clustering Model for Design of Computer Aided Detection/Diagnosis (CADe/CADx) System." MAPAN 33, no. 1 (2017): 63–75. http://dx.doi.org/10.1007/s12647-017-0230-8.

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Ren, Yi Ping, Lin Liu, Chieh Sian KOO, et al. "IMPACT OF COMPUTER-AIDED DIAGNOSIS(CADX) ON HUMAN DECISION MAKING IN THE CHARACTERIZATION OF COLON POLYPS." Gastrointestinal Endoscopy 101, no. 5 (2025): S40. https://doi.org/10.1016/j.gie.2025.03.074.

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Hina, Iftikhar, Shahid Ahmad Raza, Raza Basit, and Khan Hasan. "Multi-View Attention-based Late Fusion (MVALF) CADx system for breast cancer using deep learning." Machine Graphics and Vision 29, no. 1/4 (2021): 55–78. http://dx.doi.org/10.22630/mgv.2020.29.1.4.

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Breast cancer is a leading cause of death among women. Early detection can significantly reduce the mortality rate among women and improve their prognosis. Mammography is the first line procedure for early diagnosis. In the early era, conventional Computer-Aided Diagnosis (CADx) systems for breast lesion diagnosis were based on just single view information. The last decade evidence the use of two views mammogram: Medio-Lateral Oblique (MLO) and Cranio-Caudal (CC) view for the CADx systems. Most recent studies show the effectiveness of four views of mammogram to train CADx system with feature f

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Nguyen, Dat Tien, Jiho Choi, and Kang Ryoung Park. "Thyroid Nodule Segmentation in Ultrasound Image Based on Information Fusion of Suggestion and Enhancement Networks." Mathematics 10, no. 19 (2022): 3484. http://dx.doi.org/10.3390/math10193484.

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Computer-aided diagnosis/detection (CADx) systems have been used to help doctors in improving the quality of diagnosis and treatment processes in many serious diseases such as breast cancer, brain stroke, lung cancer, and bone fracture. However, the performance of such systems has not been completely accurate. The key factor in CADx systems is to localize positive disease lesions from the captured medical images. This step is important as it is used not only to localize lesions but also to reduce the effect of noise and normal regions on the overall CADx system. In this research, we proposed a

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Schiabel, Homero, Bruno Roberto Nepomuceno Matheus, and Fernanda Junqueira Fortes Cardoso. "Real time mass classification for mammographic images: a Driven CADx scheme." Brazilian Journal of Health Review 6, no. 3 (2023): 13909–27. http://dx.doi.org/10.34119/bjhrv6n3-429.

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Computer-Aided Diagnosis (CADx) schemes have been proposed to serve as a supplementary image analysis tool in mammography. Experienced radiologists tend to be more assertive to such schemes in assisting their interpretation rather than solely relying on their ability to detect suspicious signals. This study focuses on a simplified version of a previously developed mammography CADx scheme, which was initially designed for digitized film, but is now specifically aimed at classifying breast nodules marked as regions of interest on digital images. This “driven” CADx scheme provides prompt indicati

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Ellmann, Stephan, Michael Schlicht, Matthias Dietzel, et al. "Computer-Aided Diagnosis in Multiparametric MRI of the Prostate: An Open-Access Online Tool for Lesion Classification with High Accuracy." Cancers 12, no. 9 (2020): 2366. http://dx.doi.org/10.3390/cancers12092366.

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Computer-aided diagnosis (CADx) approaches could help to objectify reporting on prostate mpMRI, but their use in many cases is hampered due to common-built algorithms that are not publicly available. The aim of this study was to develop an open-access CADx algorithm with high accuracy for classification of suspicious lesions in mpMRI of the prostate. This retrospective study was approved by the local ethics commission, with waiver of informed consent. A total of 124 patients with 195 reported lesions were included. All patients received mpMRI of the prostate between 2014 and 2017, and transrec

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Lam, Thomas Y., Hong Xu, Wei Gong, et al. "EFFECTIVENESS OF COMPUTER-AIDED DIAGNOSIS (CADX) FOR POLYP CHARACTERIZATION DURING COLONOSCOPY. A MULTI-CENTRE RANDOMIZED CONTROLLED TRIAL." Gastrointestinal Endoscopy 101, no. 5 (2025): S287. https://doi.org/10.1016/j.gie.2025.03.505.

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Fonollà, Roger, Quirine E. W. van der Zander, Ramon M. Schreuder, et al. "A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities." Applied Sciences 10, no. 15 (2020): 5040. http://dx.doi.org/10.3390/app10155040.

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Colorectal polyps are critical indicators of colorectal cancer (CRC). Blue Laser Imaging and Linked Color Imaging are two modalities that allow improved visualization of the colon. In conjunction with the Blue Laser Imaging (BLI) Adenoma Serrated International Classification (BASIC) classification, endoscopists are capable of distinguishing benign and pre-malignant polyps. Despite these advancements, this classification still prevails a high misclassification rate for pre-malignant colorectal polyps. This work proposes a computer aided diagnosis (CADx) system that exploits the additional infor

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Rademacher, Andres, and Siegbert Faiss. "Polypendetektion mit künstlicher Intelligenz." Endo-Praxis 37, no. 01 (2021): 37–42. http://dx.doi.org/10.1055/a-1253-5316.

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ZusammenfassungDurch die Vorsorgekoloskopie lässt sich die Inzidenz und die Sterblichkeit des kolorektalen Karzinoms effektiv senken. Die Adenomdetektionsrate (ADR = engl. adenoma detection rate) stellt ein entscheidendes Qualitätskriterium der Vorsorgekoloskopie dar. Die Nutzung computerbasierender Assistenzsysteme in der Endoskopie bietet große Chancen, die Adenomdetektionsrate weiter zu steigern und für eine weitere Qualitätssicherung in der Endoskopie zu sorgen.Die theoretischen Grundlagen der künstlichen Intelligenz wurden bereits in den 1950er-Jahren gelegt, eine breite Anwendung ist jed

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Mayur Pankhania and Aditya Mehta. "Radiomics & Radiology: A Critical Step towards Integrated Healthcare." Asian Journal of Medical Radiological Research 8, no. 2 (2020): 23–30. http://dx.doi.org/10.47009/ajmrr.2020.8.2.4.

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Radiomics have shown great promise for integrated healthcare. Radiomics is defined as high-performance retrieval of significant volumes of characteristics from images and conversion of images to higher-dimensional data and subsequently mining for improved support for therapeutic judgements. It has its roots within Computer-Aided Detection (CAD)or Computer-Aided Diagnosis (CADx); it is unique in many aspects. It does not just detect and diagnose but also ventures into therapeutic, prediction, projection and modelling that can be used to generalize and reproduced. It has great potential in creat

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Tsochatzidis, Lazaros, Lena Costaridou, and Ioannis Pratikakis. "Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study." Journal of Imaging 5, no. 3 (2019): 37. http://dx.doi.org/10.3390/jimaging5030037.

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Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a

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Gautham, A., and R. Kader. "P489 Systematic review and meta-analysis of the accuracy of computer-aided diagnosis in identifying endoscopic remission in Ulcerative Colitis." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i977. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0619.

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Abstract Background Endoscopic remission (ER), the absence of active inflammation during endoscopy, is a key therapeutic target in Ulcerative Colitis (UC). ER is assessed during colonoscopy using standardised scoring systems. However, there remains significant inter-operator variability in assessing ER, for which the application of computer-aided diagnosis (CADx) could be a potential solution. A previous systematic review and meta-analysis on this topic was limited to literature up to June 2022 and did not assess the methodological quality of studies or heterogeneity amongst results. We aimed

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Vaishya, Raju, Mahesh Kumar Goenka, Anupam Sibal, Sujoy Kar, and Sangita Reddy. "Artificial Intelligence in Gastroenterology: A Comprehensive Review." NATIONAL BOARD OF EXAMINATIONS JOURNAL OF MEDICAL SCIENCES 3, no. 6 (2025): 729–45. https://doi.org/10.61770/nbejms.2025.v03.i06.011.

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Artificial Intelligence (AI) is revolutionizing gastroenterology by enhancing diagnostic accuracy and streamlining endoscopic procedures in response to the growing prevalence of gastrointestinal diseases. This comprehensive review explores AI's transformative applications, particularly in gastrointestinal endoscopy, where it addresses critical challenges such as diagnostic 'miss rates' and the overwhelming volume of medical images. Through advanced technologies like Computer-Aided detection (CADe) and Computer-Aided diagnosis (CADx) using deep learning models, AI significantly improves polyp d

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Kader, Rawen, Paul Bassett, Yusuf Kabir, et al. "RANDOMIZED CONTROLLED TRIAL OF A CLOUD-BASED ARTIFICIAL INTELLIGENCE (AI) COMPUTER-AIDED DIAGNOSIS (CADX) SYSTEM IN NON-EXPERT ENDOSCOPISTS." Gastrointestinal Endoscopy 99, no. 6 (2024): AB12—AB13. http://dx.doi.org/10.1016/j.gie.2024.04.658.

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Mridha, M. F., Akibur Rahman Prodeep, A. S. M. Morshedul Hoque, et al. "A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification." Journal of Healthcare Engineering 2022 (December 16, 2022): 1–43. http://dx.doi.org/10.1155/2022/5905230.

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Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (

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Hurtado-Aviles, José, Joaquín Roca-González, Konstantsin Sergeevich Kurochka, Jose Manuel Sanz-Mengibar, and Fernando Santonja-Medina. "Developing of a Mathematical Model to Perform Measurements of Axial Vertebral Rotation on Computer-Aided and Automated Diagnosis Systems, Using Raimondi’s Method." Radiology Research and Practice 2021 (February 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/5523775.

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Introduction. Axial vertebral rotation (AVR) is a basic parameter in the study of idiopathic scoliosis and on physical two-dimensional images. Raimondi’s tables are the most used method in the quantification of AVR. The development of computing technologies has enabled the creation of computer-aided or automated diagnosis systems (CADx) with which measurement on medical images can be carried out more quickly, simply, and with less intra and interobserver variabilities than manual methods. Although there are several publications dealing with the measurement of AVR in CADx systems, none of them

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Kim, Yun-ji, Hyun Chin Cho, and Hyun-chong Cho. "Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data." Applied Sciences 11, no. 2 (2021): 760. http://dx.doi.org/10.3390/app11020760.

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Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25

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Hizukuri, Akiyoshi, and Ryohei Nakayama. "Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network." Diagnostics 8, no. 3 (2018): 48. http://dx.doi.org/10.3390/diagnostics8030048.

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It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our database consisted of 578 breast ultrasonographic images. It included 287 malignant (217 invasive carcinomas and 70 noninvasive carcinomas) and 291 benign lesions (111 cysts and 180 fibroadenomas). In this study, the CNN constructed from four convolutional layers,

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Yuba, Mitsuru, and Kiyotaka Iwasaki. "Performance evaluation methods for improvements at post-market of artificial intelligence/machine learning-based computer-aided detection/diagnosis/triage in the United States." PLOS Digital Health 2, no. 3 (2023): e0000209. http://dx.doi.org/10.1371/journal.pdig.0000209.

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Computer-aided detection (CADe), computer-aided diagnosis (CADx), and computer-aided simple triage (CAST), which incorporate artificial intelligence (AI) and machine learning (ML), are continually undergoing post-market improvement. Therefore, understanding the evaluation and approval process of improved products is important. This study intended to conduct a comprehensive survey of AI/ML-based CAD products approved by the U.S. Food and Drug Administration (FDA) that had been improved post-market to gain insights into the efficacy and safety required for market approval. A survey of the produc

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Elamin, Sami, Mindy Duffourc, Tyler Berzin, Mark Geissler, and Sara Gerke. "Artificial Intelligence and Medical Liability in Gastrointestinal Endoscopy." Clinical Gastroenterology and Hepatology 22, no. 6 (2024): 1165–69. https://doi.org/10.1016/j.cgh.2024.03.011.

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<strong>Abstract: </strong>Integrating artificial intelligence (AI) in gastrointestinal (GI) endoscopy signifies a critical advancement for gastroenterology practice. Algorithms for computer-aided detection (CADe) of colorectal polyps have already been introduced in routine clinical care, and a wide variety of other AI applications for GI endoscopy are in advanced phases of development. There is particular interest in whether computer-aided diagnosis (CADx) could replace traditional histopathology for diagnosing small colon polyps, supporting “resect and discard” or “dia

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Lee, Chien-Cheng, Sz-Han Chen, and Yu-Chun Chiang. "Classification of Liver Disease from CT Images Using a Support Vector Machine." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 4 (2007): 396–402. http://dx.doi.org/10.20965/jaciii.2007.p0396.

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We propose a classifier based on the support vector machine (SVM) for automatic classification in liver disease. The SVM, stemming from statistical learning theory, involves state-of-the-art machine learning. The classifier is a part of computer-aided diagnosis (CADx), which assists radiologists in accurately diagnosing liver disease. We formulate discriminating between cysts, hepatoma, cavernous hemangioma, and normal tissue as a supervised learning problem, and apply SVM to classifying the diseases using gray level and co-occurrence matrix features and region-based shape descriptors, calcula

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Ooi Wei Herng, Aimi Salihah Abdul Nasir, and Abdul Syafiq Abdull Sukor. "Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms." Journal of Advanced Research in Applied Sciences and Engineering Technology 44, no. 1 (2024): 77–93. http://dx.doi.org/10.37934/araset.44.1.7793.

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Computer aided diagnosis (CADx) become one the most famous method in diagnostic medical field due to the high reliability and efficiency. Recently, the coronavirus disease (COVID-19) has become severe global pandemic. Particularly, the Chest X-ray (CXR) imaging has become an essentiality in COVID-19 detection. As a result, the convergence of CADx technology with Chest X-ray analysis has achieved great efficiency in COVID-19 diagnosis. Therefore, the research value of CADx in COVID-19 diagnosis is exceptionally high. This study aims to evaluate different k-means based clustering algorithms and

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Fraioli, F., G. Serra, and R. Passariello. "CAD (computed-aided detection) and CADx (computer aided diagnosis) systems in identifying and characterising lung nodules on chest CT: overview of research, developments and new prospects." La radiologia medica 115, no. 3 (2010): 385–402. http://dx.doi.org/10.1007/s11547-010-0507-2.

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Baumer, Sebastian, Kilian Streicher, Saleh A. Alqahtani, et al. "Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center." Endoscopy International Open 11, no. 09 (2023): E818—E828. http://dx.doi.org/10.1055/a-2096-2960.

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Abstract Background and study aims Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice. Patients and methods This prospective, non-randomized study was performed at a tertiary academic endoscopy center from March to August 2022. We included patients receiving a colonoscopy. Polypectomy had to be performed in all polyps.

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Gimeno-García, Antonio Z., Anjara Hernández-Pérez, David Nicolás-Pérez, and Manuel Hernández-Guerra. "Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?" Cancers 15, no. 8 (2023): 2193. http://dx.doi.org/10.3390/cancers15082193.

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Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain

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Sajid, Muhammad Zaheer, Imran Qureshi, Qaisar Abbas, et al. "Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture." Diagnostics 13, no. 8 (2023): 1439. http://dx.doi.org/10.3390/diagnostics13081439.

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Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms. An ophthalmologist often makes the diagnosis of eye-related diseases by analyzing fundus images to identify the stages and symptoms of HR. The likelihood of vision loss can significantly decrease the initial detection of HR. In the past, a few computer-aided diagnostics (CADx) systems were developed to automatically detect

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Dounis, Anastasios, Andreas-Nestor Avramopoulos, and Maria Kallergi. "Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms." Applied Sciences 13, no. 18 (2023): 10401. http://dx.doi.org/10.3390/app131810401.

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Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abno

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Bahaalddin, Bestan, and Hawkar Ahmed. "Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation." UKH Journal of Science and Engineering 4, no. 2 (2020): 178–87. http://dx.doi.org/10.25079/ukhjse.v4n2y2020.pp178-187.

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Mammography is the most effective procedure for the early detection of breast cancer. In this paper an efficient a Computer Aided Diagnosis (CADx) system is proposed to discriminate between benign and malignant. The system comprises mainly of three steps: preprocessing of the images, feature extraction, and finally classification and performance analysis. The case sample mammographic images, originating from the mini MIAS (Mammographic Image Analysis Society) database. In the preprocessing phase the ROI is cropped and resized by 128 x 128. at the very beginning of the feature extraction proces

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Roy, Shotabdi, and KC Santosh. "Analyzing Overlaid Foreign Objects in Chest X-Rays—Clinical Significance and Artificial Intelligence Tools." Healthcare 11, no. 3 (2023): 308. http://dx.doi.org/10.3390/healthcare11030308.

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The presence of non-biomedical foreign objects (NBFO), such as coins, buttons and jewelry, and biomedical foreign objects (BFO), such as medical tubes and devices in chest X-rays (CXRs), make accurate interpretation difficult, as they do not indicate known biological abnormalities like excess fluids, tuberculosis (TB) or cysts. Such foreign objects need to be detected, localized, categorized as either NBFO or BFO, and removed from CXR or highlighted in CXR for effective abnormality analysis. Very specifically, NBFOs can adversely impact the process, as typical machine learning algorithms would

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Park, Jae-beom, Han-sung Lee, and Hyun-chong Cho. "Investigating Effective Data Augmentation Techniques for Accurate Gastric Classification in the Development of a Deep Learning-Based Computer-Aided Diagnosis System." Applied Sciences 13, no. 22 (2023): 12325. http://dx.doi.org/10.3390/app132212325.

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Gastric cancer is a significant health concern, particularly in Korea, and its accurate detection is crucial for effective treatment. However, a gastroscopic biopsy can be time-consuming and may, thus, delay diagnosis and treatment. Thus, this study proposed a gastric cancer diagnostic method, CADx, to facilitate a more efficient image analysis. Owing to the challenges in collecting medical image data, small datasets are often used in this field. To overcome this limitation, we used AutoAugment’s ImageNet policy and applied cut-and-paste techniques using a sliding window algorithm to further i

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Mandeel, Thulfiqar H., Salah M. Awad, and Shama Naji. "Pneumonia binary classification using multi-scale feature classification network on chest x-ray images." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1469. http://dx.doi.org/10.11591/ijai.v11.i4.pp1469-1477.

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<span lang="EN-US">According to the world health organization, pneumonia was the cause for 14% of all deaths of children under 5 years old. A computer-aided diagnosis (CADx) system can help the radiologist in the detection of pneumonia in chest radiographs by serving as a second opinion. The typical CADx is based on transfer learning which is done by transferring the learning of feature extraction from one task with plenty of available data to a related task with a scarcity of data. This approach has two limitations which are first, blocking the transferred model from extracting the feat

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Rêgo, Sílvia, Matilde Monteiro-Soares, Marco Dutra-Medeiros, Filipe Soares, Cláudia Camila Dias, and Francisco Nunes. "Implementation and Evaluation of a Mobile Retinal Image Acquisition System for Screening Diabetic Retinopathy: Study Protocol." Diabetology 3, no. 1 (2022): 1–16. http://dx.doi.org/10.3390/diabetology3010001.

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Screening diabetic retinopathy, a major cause of blindness, is time-consuming for ophthalmologists and has some constrains in achieving full coverage and attendance. The handheld fundus camera EyeFundusScope was recently developed to expand the scale of screening, drawing on images acquired in primary care and telescreening made by ophthalmologists or a computer-aided diagnosis (CADx) system. This study aims to assess the diagnostic accuracy of the interpretation of images captured using EyeFundusScope and perform its technical evaluation, including image quality, functionality, usability, and

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