Relevant bibliographies by topics / Computer aided detection system

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computer aided detection system'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Computer aided detection system"

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Basha, C. M. A. K. Zeelan, Maruthi Padmaja, and G. N. Balaji. "Computer Aided Fracture Detection System." Journal of Medical Imaging and Health Informatics 8, no. 3 (2018): 526–31. http://dx.doi.org/10.1166/jmihi.2018.2324.

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Li, Feng, Roger Engelmann, Samuel G. Armato, and Heber MacMahon. "Computer-Aided Nodule Detection System." Academic Radiology 22, no. 4 (2015): 475–80. http://dx.doi.org/10.1016/j.acra.2014.11.008.

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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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Carrillo-de-Gea, Juan Manuel, Ginés García-Mateos, José Luis Fernández-Alemán, and José Luis Hernández-Hernández. "A Computer-Aided Detection System for Digital Chest Radiographs." Journal of Healthcare Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8208923.

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Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a c
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Pan, Tony C., Metin N. Gurcan, Stephen A. Langella, et al. "GridCAD: Grid-based Computer-aided Detection System." RadioGraphics 27, no. 3 (2007): 889–97. http://dx.doi.org/10.1148/rg.273065153.

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Scotti, James V. "Computer Aided Near Earth Object Detection." Symposium - International Astronomical Union 160 (1994): 17–30. http://dx.doi.org/10.1017/s0074180900046428.

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The Spacewatch program at the University of Arizona has pioneered automatic methods of detecting Near Earth Objects. Our software presently includes three modes of object detection: automatic motion identification; automatic streak identification; and visual streak identification. For automatic motion detection at sidereal drift rates, the 4σ detection threshold is near magnitude V = 20.9 for nearly stellar asteroid images. The automatic streak detection is able to locate streaks whose peak signal is above ~4σ and whose length is longer than about 10 pixels. Some visually detected streaks have
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Omar, Raad Kadhim, Jassim Motlak Hassan, and Karam Abdall Kasim. "Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4734–45. https://doi.org/10.11591/ijece.v12i5.pp4734-4745.

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This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection proces
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Kadhim, Omar Raad, Hassan Jassim Motlak, and Kasim Karam Abdalla. "Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4734. http://dx.doi.org/10.11591/ijece.v12i5.pp4734-4745.

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This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection proces
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Marrocco, Claudio, Mario Molinara, Ciro D’Elia, and Francesco Tortorella. "A computer-aided detection system for clustered microcalcifications." Artificial Intelligence in Medicine 50, no. 1 (2010): 23–32. http://dx.doi.org/10.1016/j.artmed.2010.04.007.

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Myat, Mon Kyaw. "Computer-Aided Detection system for Hemorrhage contained region." International Journal of Computational Science and Information Technology (IJCSITY) 1, February (2013): 1–9. https://doi.org/10.5281/zenodo.3626722.

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<strong>ABSTRACT </strong> This paper aims to introduce the automated method to detect and classify an abnormality (hemorrhage) of stroke in brain CT image. Initially, the image is pre-processed to remove film artifacts and skull region. Before the abnormality region is segmented, the image is pre-segmented. In this stage, the image is subdivided into four regions to find the region that has the possibility for inclusion of abnormal areas. Thus, the unnecessary regions are no need to search and segment. This paper proposes pre-segmentation steps for the detection of abnormal region in brain im
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Dissertations / Theses on the topic "Computer aided detection system"

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Asl, Babak Ghafary. "A Computer Aided Detection System for Cerebral Microbleeds in Brain MRI." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6053.

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Advances in MR technology have improved the potential for visualization of small lesions in brain images. This has resulted in the opportunity to detect cerebral microbleeds (CMBs), small hemorrhages in the brain that are known to be associated with risk of ischemic stroke and intracerebral bleeding. Currently, no computerized method is available for fully- or semi-automated detection of CMBs. In this paper, we propose a CAD system for the detection of CMBs to speed up visual analysis in population-based studies. Our method consists of three steps: (i) skull-stripping (ii) initial candidate se
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GIANNINI, VALENTINA. "Computer Aided Diagnosis systems for MR cancer detection." Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2496445.

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The research activity conducted during my PhD aims to develop two different Computer Aided Diagnosis (CAD) systems for breast and prostate cancer diagnosis using Magnetic Resonance Imaging. During the first part of this thesis I will illustrate a fully automatic CAD system for breast cancer detection and diagnosis with Dynamic Contrast Enhanced MRI (DCE-MRI) developed by our group. The main goal of a CAD system is lesions detection and characterization. The processing pipeline includes automatic segmentation of the breast and axillary regions, registration of unenhanced and contrast-enhanced fra
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Rabbani, Seyedeh Parisa. "Effect of image variation on computer aided detection systems." Thesis, KTH, Skolan för teknik och hälsa (STH), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123546.

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Computer Aided Detection (CAD) systems are expecting to gain significant importance in terms of reducing the work load of radiologists and enabling the large screening programs. A large share of CAD systems are based on learning from examples, to enables the decision making between the images with or without disease. Images are simplified to numerical descriptors (features vectors) and the system is trained with these features. The common practical problem with CAD systems is training the system with a data from a specific source and testing it on a data from a different source; the variations
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Jirari, Mohammed. "Computer Aided System For Detecting Masses In Mammograms." Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1212099614.

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He, Qinfen. "Computer-aided instrument system for the detection and analysis of partial discharge activity." Thesis, Glasgow Caledonian University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282747.

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Casiraghi, E. "A computer aided diagnosis system for lung nodules detection in postero anterior chest radiographs." Doctoral thesis, Università degli Studi di Milano, 2004. http://hdl.handle.net/2434/451239.

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This thesis describes a Computer Aided System aimed at lung nodules detection. The fully automatized method developed to search for nodules is composed by four steps. They are the segmentation of the lung field, the enhancement of the image, the extraction of the candidate regions, and the selection between them of the regions with the highest chance to be True Positives. The steps of segmentation, enhancement and candidates extraction are based on multi-scale analysis. The common assumption underlying their development is that the signal representing the details to be detected by each
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Xu, Chong. "Reduced-complexity near-optimal Ant-Colony-aided multi-user detection for CDMA systems." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/206015/.

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Reduced-complexity near-maximum-likelihood Ant-Colony Optimization (ACO) assisted Multi-User Detectors (MUDs) are proposed and investigated. The exhaustive search complexity of the optimal detection algorithm may be deemed excessive for practical applications. For example, a Space-Time Block Coded (STBC) two transmit assisted K = 32-user system has to search through the candidate-space for finding the final detection output during 264 times per symbol duration by invoking the Euclidean-distance-calculation of a 64-element complex-valued vector. Hence, a nearoptimal or near-ML MUDs are required
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Qi, Xuguang. "AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/504.

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Midline shift (MLS), the amount of displacement of the brain’s midline from its normal symmetric position due to illness or injury, is an important index for clinicians to assess the severity of traumatic brain injury (TBI). In this dissertation, an automated computer-aided midline shift estimation system is proposed. First, a CT slice selection algorithm (SSA) is designed to automatically select a subset of appropriate CT slices from a large number of raw images for MLS detection. Next, ideal midline detection is implemented based on skull bone anatomical features and global rotation assumpt
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Thaung, Ludwig. "Advanced Data Augmentation : With Generative Adversarial Networks and Computer-Aided Design." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170886.

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CNN-based (Convolutional Neural Network) visual object detectors often reach human level of accuracy but need to be trained with large amounts of manually annotated data. Collecting and annotating this data can frequently be time-consuming and financially expensive. Using generative models to augment the data can help minimize the amount of data required and increase detection per-formance. Many state-of-the-art generative models are Generative Adversarial Networks (GANs). This thesis investigates if and how one can utilize image data to generate new data through GANs to train a YOLO-based (Yo
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Wen, Yiding. "Detecting microcalcifications in digitised mammograms by a computer aided diagnostic system." Thesis, The University of Sydney, 1999. https://hdl.handle.net/2123/27591.

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Breast cancer is now one of the most common forms of cancer and the leading cause of mortality in women in the developed countries. Early detection of breast cancer is currently the way to reduce breast cancer mortality and enhance the cure rate. Mammogram screening is widely recognized as the most reliable method for early detection of lesions and clustered microcalcifications, which are the two prominent symptoms of breast cancer. This thesis presents an image processing procedure for the automatic detection of clustered microcalcifications in digitized mammograms. This method consi
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Books on the topic "Computer aided detection system"

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Natke, H. G. Model-aided diagnosis of mechanical systems: Fundamentals, detection, localization, and assessment. Springer Verlag, 1997.

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Coderre, David G. Computer Aided Fraud Prevention and Detection. John Wiley & Sons, Ltd., 2009.

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Coderre, David, ed. Computer-Aided Fraud Prevention and Detection. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119203971.

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Elseid, Arwa Ahmed Gasm, and Alnazier Osman Mohammed Hamza. Computer-Aided Glaucoma Diagnosis System. CRC Press, 2020. http://dx.doi.org/10.1201/9780367406288.

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Agaian, S. S., and Jinshan Tang. Computer-aided cancer detection and diagnosis: Recent advances. SPIE Press, 2014.

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Nassirharand, Amir. Computer-aided Nonlinear Control System Design. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2149-7.

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Robin, Cooper. CABIS, computer-aided bookkeeping instruction system. Addison-Wesley, 1987.

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Tebbutt, Colin. Expert aided control system design. Springer-Verlag, 1994.

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1943-, Knuth E., and Radó Péter, eds. Computer-aided specification techniques. World Scientific, 1985.

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Lai, John J. Computer system performance assessment for computer aided manufacture. UMIST, 1996.

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Book chapters on the topic "Computer aided detection system"

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Bhattacharjee, Ananya, and Swanirbhar Majumder. "Automated Computer-Aided Lung Cancer Detection System." In Advances in Communication, Devices and Networking. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3450-4_46.

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Puttagunta, Murali Krishna, S. Ravi, A. Anbarasi, and C. Nelson Kennedy Babu. "Detection of Tuberculosis Using Computer-Aided Diagnosis System." In Big data management in Sensing. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337355-3.

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Stanford, Caleb, and Margus Veanes. "Incremental Dead State Detection in Logarithmic Time." In Computer Aided Verification. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37703-7_12.

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AbstractIdentifying live and dead states in an abstract transition system is a recurring problem in formal verification; for example, it arises in our recent work on efficiently deciding regex constraints in SMT. However, state-of-the-art graph algorithms for maintaining reachability information incrementally (that is, as states are visited and before the entire state space is explored) assume that new edges can be added from any state at any time, whereas in many applications, outgoing edges are added from each state as it is explored. To formalize the latter situation, we propose guided incremental digraphs (GIDs), incremental graphs which support labeling closed states (states which will not receive further outgoing edges). Our main result is that dead state detection in GIDs is solvable in $$O(\log m)$$ O ( log m ) amortized time per edge for m edges, improving upon $$O(\sqrt{m})$$ O ( m ) per edge due to Bender, Fineman, Gilbert, and Tarjan (BFGT) for general incremental directed graphs.We introduce two algorithms for GIDs: one establishing the logarithmic time bound, and a second algorithm to explore a lazy heuristics-based approach. To enable an apples-to-apples experimental comparison, we implemented both algorithms, two simpler baselines, and the state-of-the-art BFGT baseline using a common directed graph interface in Rust. Our evaluation shows 110-530x speedups over BFGT for the largest input graphs over a range of graph classes, random graphs, and graphs arising from regex benchmarks.
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Beneš, Nikola, Luboš Brim, Samuel Pastva, and David Šafránek. "Computing Bottom SCCs Symbolically Using Transition Guided Reduction." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_24.

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AbstractDetection of bottom strongly connected components (BSCC) in state-transition graphs is an important problem with many applications, such as detecting recurrent states in Markov chains or attractors in dynamical systems. However, these graphs’ size is often entirely out of reach for algorithms using explicit state-space exploration, necessitating alternative approaches such as the symbolic one.Symbolic methods for BSCC detection often show impressive performance, but can sometimes take a long time to converge in large graphs. In this paper, we provide a symbolic state-space reduction method for labelled transition systems, called interleaved transition guided reduction (ITGR), which aims to alleviate current problems of BSCC detection by efficiently identifying large portions of the non-BSCC states.We evaluate the suggested heuristic on an extensive collection of 125 real-world biologically motivated systems. We show that ITGR can easily handle all these models while being either the only method to finish, or providing at least an order-of-magnitude speedup over existing state-of-the-art methods. We then use a set of synthetic benchmarks to demonstrate that the technique also consistently scales to graphs with more than $$2^{1000}$$ 2 1000 vertices, which was not possible using previous methods.
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Stoller, Scott D., and Yanhong A. Liu. "Efficient symbolic detection of global properties in distributed systems." In Computer Aided Verification. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0028758.

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Jin, Peng, Jiaxu Tian, Dapeng Zhi, Xuejun Wen, and Min Zhang. "Trainify: A CEGAR-Driven Training and Verification Framework for Safe Deep Reinforcement Learning." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13185-1_10.

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AbstractDeep Reinforcement Learning (DRL) has demonstrated its strength in developing intelligent systems. These systems shall be formally guaranteed to be trustworthy when applied to safety-critical domains, which is typically achieved by formal verification performed after training. This train-then-verify process has two limits: (i) trained systems are difficult to formally verify due to their continuous and infinite state space and inexplicable AI components (i.e., deep neural networks), and (ii) the ex post facto detection of bugs increases both the time- and money-wise cost of training and deployment. In this paper, we propose a novel verification-in-the-loop training framework called Trainify for developing safe DRL systems driven by counterexample-guided abstraction and refinement. Specifically, Trainify trains a DRL system on a finite set of coarsely abstracted but efficiently verifiable state spaces. When verification fails, we refine the abstraction based on returned counterexamples and train again on the finer abstract states. The process is iterated until all predefined properties are verified against the trained system. We demonstrate the effectiveness of our framework on six classic control systems. The experimental results show that our framework yields more reliable DRL systems with provable guarantees without sacrificing system performance such as cumulative reward and robustness than conventional DRL approaches.
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Carrera, Enrique V., and David Ron-Domínguez. "A Computer Aided Diagnosis System for Skin Cancer Detection." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05532-5_42.

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Maruf-uz-zaman and Rajeev Ranjan. "A Computer Aided Detection System for Lung Nodules Classification." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-75861-4_19.

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Gavilán, M., D. Balcones, M. A. Sotelo, et al. "Surface Classification for Road Distress Detection System Enhancement." In Computer Aided Systems Theory – EUROCAST 2011. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27549-4_77.

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İlkin, Sümeyya, Fidan Kaya Gülağız, Fatma Selin Hangişi, and Suhap Şahin. "Computer Aided Wound Area Detection System for Dermatological Images." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77712-2_77.

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Conference papers on the topic "Computer aided detection system"

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Kasturi, Akhil, Ali Vosoughi, Nathan Hadjiyski, and Axel Wismüller. "ETT-LDx: transformer-based landmark detection system for endotracheal tube placement verification in chest radiographs." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3047472.

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Esselaar, Esmee, Tim J. M. Jaspers, Carolus H. J. Kusters, et al. "2.5D mapping of the esophagus as imaging quality and completeness assuring extension for endoscopic computer-aided detection systems." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3046636.

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Lin, Wei-Shiang, Yi-Hsiang Cheng, Zhen-Yu Hung, and Yuan Yao. "Developing a Digital Twin System Based on a Physics-informed Neural Network for Pipeline Leakage Detection." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.126840.

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As the demand for resources continues to grow, pipelines have become critical for transporting water, fossil fuels, and chemicals. Monitoring pipeline systems is essential, as leaks can lead to severe environmental damage and safety hazards. This study aims to develop a pipeline leakage detection system based on digital twin technology and Physics-Informed Neural Networks (PINNs). By embedding physical principles, such as the continuity and momentum equations derived from the Navier-Stokes equation, into the neural network's loss function, the model can predict pressure and flow dynamics with
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Yang, Borui, and Jinsong Zhao. "A Fault Detection Method Based on Key Variable Forecasting." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.105964.

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This paper presents a novel fault detection method based on key variable forecasting models. The approach integrates future forecasts of key variables into a time window, allowing for early fault detection without modifying the offline training phase of the existing fault detection model. By incorporating predicted data into the detection process, the proposed method significantly improves fault detection rates and reduces detection delays. Experiments using the Continuous Stirred Tank Heater (CSTH) system demonstrate the superiority of our method over traditional approaches, showing the advan
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Ignatious, Sruthi, and Robin Joseph. "Computer aided lung cancer detection system." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342723.

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Ren, Yinhao, Rui Hou, Dehan Kong, et al. "Multiview mammographic mass detection based on a single shot detection system." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2513136.

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Zhao, Liang, Zhennan Yan, Matthias Wolf, Yiyuan Zhao, and Yiqiang Zhan. "A deep-learning based automatic pulmonary nodule detection system." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2295368.

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Rao, M. K., and C. C. Goh. "Computer Aided Laser Ranging And Detection System." In Technical Symposium Southeast, edited by Richard J. Becherer and Robert C. Harney. SPIE, 1987. http://dx.doi.org/10.1117/12.940590.

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Matsuhiro, Mikio, Hidenobu Suzuki, Yoshiki Kawata, et al. "Computer aided detection system for Osteoporosis using low dose thoracic 3D CT images." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2293483.

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Riegler, Michael, Konstantin Pogorelov, Jonas Markussen, et al. "Computer aided disease detection system for gastrointestinal examinations." In MMSys'16: Multimedia Systems Conference 2016. ACM, 2016. http://dx.doi.org/10.1145/2910017.2910629.

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Reports on the topic "Computer aided detection system"

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Lau, Beverly. Optimization of Breast Tomosynthesis Imaging Systems for Computer-Aided Detection. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada545786.

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Chan, Heang P. Digital Mammography: Development of an Advanced Computer-Aided System for Breast Cancer Detection. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425978.

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Wei, Jun. Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada438612.

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Wei, Jun, Wendy M. Kohrt, L. M. Glode, Robert S. Schwartz, and Daniel W. Barry. Development of a Computer-aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada510061.

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Wei, Jun. Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada458398.

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Wei, Jun. Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada472884.

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Moshura, Mariia, Yana Terleeva, and Olena Nesterova. Evaluating the performance of computer detection software implementation in triaging chest X-ray images in TB screening program in Ukraine. Public Health Center of the Ministry of Health of Ukraine, 2024. https://doi.org/10.63263/tb0000001.

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Modern technologies for diagnosing tuberculosis and effective treatment regimens contribute to the fight against the disease in various ways. In general, modern diagnostic and treatment technologies increase the chances of early detection and treatment of tuberculosis, reducing morbidity and improving quality of life. This study aimed to determine the optimal model for the implementation of computer-aided diagnostic (CAD) systems in Ukrainian TB facilities in Lviv and Sumy oblasts. The study had the following components: Data collection from TB facilities included an assessment of the current
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Penfield, Jr, Antoniadis Paul, Gershwin Dimitri A., et al. Computer-Aided Fabrication System Implementation. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada202128.

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Penfield, Jr, and Paul. Computer-Aided Fabrication System Implementation. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203651.

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Briggs, M. G. Computer-Aided dispatching system design specification. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/325291.

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