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Journal articles on the topic 'Computer aided diagnosis tools'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Colombi, Davide, Maurizio Marvisi, Sara Ramponi, et al. "Computer-Aided Evaluation of Interstitial Lung Diseases." Diagnostics 15, no. 7 (2025): 943. https://doi.org/10.3390/diagnostics15070943.

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The approach for the diagnosis and treatment of interstitial lung diseases (ILDs) has changed in recent years, mainly for the identification of new entities, such as interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF). Clinicians and radiologists are facing new challenges for the screening, diagnosis, prognosis, and follow-up of ILDs. The detection and classification of ILAs or the identification of fibrosis progression at high-resolution computed tomography (HRCT) is difficult, with high inter-reader variability, particularly for non-expert radiologists. In the las

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Mun, Seong K., and Dow-Mu Koh. "Special Issue: “Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging”." Diagnostics 12, no. 6 (2022): 1331. http://dx.doi.org/10.3390/diagnostics12061331.

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The radiology imaging community has been developing computer-aided diagnosis (CAD) tools since the early 1990s before the imagination of artificial intelligence (AI) fueled many unbound healthcare expectations and other industries [...]

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Ioanovici, Andrei-Constantin, Andrei-Marian Feier, Ioan Țilea, and Daniela Dobru. "Computer-Aided Diagnosis in Colorectal Cancer: Current Concepts and Future Prospects." Journal of Interdisciplinary Medicine 2, no. 3 (2017): 245–49. http://dx.doi.org/10.1515/jim-2017-0057.

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Abstract Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers’ focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, dif

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Molino, F., D. Furia, F. Bar, S. Battista, N. Cappello, and G. Molino. "Computer-Aided Diagnosis in Jaundice: Comparison of Knowledge-based and Probabilistic Approaches." Methods of Information in Medicine 35, no. 01 (1996): 41–51. http://dx.doi.org/10.1055/s-0038-1634634.

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AbstractThe study reported in this paper is aimed at evaluating the effectiveness of a knowledge-based expert system (ICTERUS) in diagnosing jaundiced patients, compared with a statistical system based on probabilistic concepts (TRIAL). The performances of both systems have been evaluated using the same set of data in the same number of patients. Both systems are spin-off products of the European project Euricterus, an EC-COMACBME Project designed to document the occurrence and diagnostic value of clinical findings in the clinical presentation of jaundice in Europe, and have been developed as

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Lorenzo-Ramírez, Marlene Alejandra, Valeria Michelle Pérez-Alvarado, Fermín Paúl Ramírez-Villavicencio, Jesse Sánchez-Cárdenas, Alma Teresa Orozco-Padilla, and Gener J. Avilés-Rodríguez. "Computer aided diagnosis in mammograms for breast cancer screening." Revista de la Facultad de Medicina Humana 24, no. 4 (2024): 159–79. http://dx.doi.org/10.25176/rfmh.v24i4.6554.

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Securing access to quality healthcare services, particularly for essential screening tests like mammograms, presents a significant challenge in developing nations. Women often encounter extensive waiting periods, sometimes extending for several months, to undergo a mammogram. This crucial test plays a pivotal role in the early detection of breast cancer, where timely diagnosis is crucial for effective treatment and enhanced survival prospects. Delays in obtaining a diagnosis can significantly impact the health of patients and their well-being, underscoring the importance of early detection. Co

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Bartolini, Ilaria, and Andrea Di Luzio. "CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy." Computers 10, no. 4 (2021): 51. http://dx.doi.org/10.3390/computers10040051.

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Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facil

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Jiménez-Gaona, Yuliana, María José Rodríguez-Álvarez, and Vasudevan Lakshminarayanan. "Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review." Applied Sciences 10, no. 22 (2020): 8298. http://dx.doi.org/10.3390/app10228298.

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This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more de

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Lee, Juhun, Robert M. Nishikawa, Ingrid Reiser, and John M. Boone. "Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT." Medical Physics 44, no. 5 (2017): 1846–56. http://dx.doi.org/10.1002/mp.12214.

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Ribeiro, Ricardo T., Rui Tato Marinho, and J. Miguel Sanches. "An Ultrasound-Based Computer-Aided Diagnosis Tool for Steatosis Detection." IEEE Journal of Biomedical and Health Informatics 18, no. 4 (2014): 1397–403. http://dx.doi.org/10.1109/jbhi.2013.2284785.

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Chandrasekar, Devadharshini, Dr Burnice Nalina Kumari, and Nikita Ravi et. al. "Revolutionizing Periodontics: The Impact of Digitalization on Diagnosis and Treatment." International Journal of Healthcare and Medical Sciences, no. 103 (September 15, 2024): 33–36. http://dx.doi.org/10.32861/ijhms.103.33.36.

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The objective of this review is to explore the impact of digitalization on periodontics, with a particular focus on its role in enhancing both diagnosis and treatment. Drawing from a range of recent technological advancements, the study reviews the implementation of advanced imaging techniques and artificial intelligence (AI) in clinical practice. Key digital tools such as cone-beam computed tomography (CBCT), digital intraoral scanners, computer-aided design and manufacturing (CAD/CAM), and 3D printing are examined for their contributions to the field. The results indicate that the integratio

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Dolley Srivastava. "A Computer-Aided Diagnosis for Cardiovascular and Hepatic Disorders using Boosted Ensemble Deep Learning." Journal of Information Systems Engineering and Management 10, no. 1s (2024): 142–52. https://doi.org/10.52783/jisem.v10i1s.109.

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In recent years, machine learning has gained traction as a potential tool for improving the accuracy and timeliness of illness diagnoses. The use of machine learning for the diagnosis of cardiovascular and renal disorders is critically examined in this research. To enhance patient outcomes, it is essential to diagnose cardiovascular and hepatic illnesses early and accurately. The interpretation of complicated clinical data and the identification of detailed patterns indicative of these disorders, however, may be difficult for standard diagnostic approaches. This study thoroughly tests three cu

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G. P, Vishnu Prasad, Kurapati Vishnu Sai Reddy, A. M. Kiruthik, and Dr J. Arun Nehru. "Prediction of Kidney Stones Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1037–44. http://dx.doi.org/10.22214/ijraset.2022.42416.

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Abstract: Kidney stones are a prevalent disease all over the world, resulting in many of us being rushed to the hospital in excruciating pain. Calculus illness is diagnosed using a variety of imaging modalities. For the interpretation and thorough diagnosis of the photos, specialists are required. Computer-aided diagnosis systems are practical ways that can be utilized as supplemental tools to aid clinicians in their diagnosis. During this project, the deep learning (DL) technique was used to propose an automatic diagnosis of kidney stones using coronal X-ray (CT) pictures, which has made a gr

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Owais, Muhammad, Muhammad Arsalan, Tahir Mahmood, Jin Kyu Kang, and Kang Ryoung Park. "Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation." Journal of Medical Internet Research 22, no. 11 (2020): e18563. http://dx.doi.org/10.2196/18563.

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Background The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tu

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Mansoor Alhammadi, Mohammed. "Computer-Aided Diagnosis of Acute Lymphoblastic Leukemiaby Using a Novel CAE-CNN Framework." Iraqi Journal for Computers and Informatics 50, no. 2 (2024): 186–206. https://doi.org/10.25195/ijci.v50i2.502.

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Acute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing efficient management and treatment methods. The conventional diagnostic tools, based on the morphological and cytochemical investigation of blood and bone smears, are usually not specific and laborious. Thus, they often result in diagnostic errors and delay in treatment initiation. In this paper, ALL-diagnosing methods based on the convolutional autoencoder (CAE) was proposed to reduce the amount of data, and then convolutional neural ne

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Huang, Xing, Tsung-Yi Ho, Wenzhong Guo, Bing Li, Krishnendu Chakrabarty, and Ulf Schlichtmann. "Computer-aided Design Techniques for Flow-based Microfluidic Lab-on-a-chip Systems." ACM Computing Surveys 54, no. 5 (2021): 1–29. http://dx.doi.org/10.1145/3450504.

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As one of the most promising lab-on-a-chip systems, flow-based microfluidic biochips are being increasingly used for automatically executing various laboratory procedures in biology and biochemistry, such as enzyme-linked immunosorbent assay, point-of-care diagnosis, and so on. As manufacturing technology advances, the characteristic dimensions of biochip systems keep shrinking, and tens of thousands of microvalves can now be integrated into a coin-sized microfluidic platform, making the conventional manual-based chip design no longer applicable. Accordingly, computer-aided design (CAD) of mic

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Santos, Marcel Koenigkam, José Raniery Ferreira Júnior, Danilo Tadao Wada, Ariane Priscilla Magalhães Tenório, Marcello Henrique Nogueira Barbosa, and Paulo Mazzoncini de Azevedo Marques. "Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine." Radiologia Brasileira 52, no. 6 (2019): 387–96. http://dx.doi.org/10.1590/0100-3984.2019.0049.

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Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diag

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Alexe, Gabriela, James Monaco, Scott Doyle, et al. "Towards Improved Cancer Diagnosis and Prognosis Using Analysis of Gene Expression Data and Computer Aided Imaging." Experimental Biology and Medicine 234, no. 8 (2009): 860–79. http://dx.doi.org/10.3181/0902-mr-89.

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With the increasing cost effectiveness of whole slide digital scanners, gene expression microarray and SNP technologies, tissue specimens can now be analyzed using sophisticated computer aided image and data analysis techniques for accurate diagnoses and identification of prognostic markers and potential targets for therapeutic intervention. Microarray analysis is routinely able to identify biomarkers correlated with survival and reveal pathways underlying pathogenesis and invasion. In this paper we describe how microarray profiling of tumor samples combined with simple but powerful methods of

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Iakovidis, D. K., T. Goudas, C. Smailis, and I. Maglogiannis. "Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/286856.

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Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifyin

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Brüllmann, Dan Dominik, Catharina I. D. Weichert, and Monika Daubländer. "Intraoral Cameras as a Computer-Aided Diagnosis Tool for Root Canal Orifices." Journal of Dental Education 75, no. 11 (2011): 1452–57. http://dx.doi.org/10.1002/j.0022-0337.2011.75.11.tb05202.x.

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Meira, Marcilio de Oliveira, Anne Magaly de Paula Canuto, Bruno Motta de Carvalho, and Roberto Levi Cavalcanti Jales. "Comparison of Machine Learning predictive methods to diagnose the Attention Deficit/Hyperactivity Disorder levels using SPECT." Research, Society and Development 11, no. 8 (2022): e54811831258. http://dx.doi.org/10.33448/rsd-v11i8.31258.

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ADHD (attention deficit hyperactivity disorder) is a neurodevelopmental disorder characterized by harmful levels of inattention, disorganization, and/or hyperactivity-impulsivity. In childhood, these symptoms often overlap with those of other disorders, and they tend to persist into adulthood, interfering with relationships and academic and work life. Diagnosis, traditionally made by assessing the patient, i.e., testing and listening to relatives and teachers, has already been aided by neuroimaging. However, the visual analysis of such images to make a psychiatric diagnosis is a complex and so

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Pavlov, A. E., B. V. Dagbaev, and I. I. Starkova. "COMPREHENSIVE SURVEY OF FREESTYLE WRESTLERS USING COMPUTER-AIDED PULSE DIAGNOSIS SYSTEM (TIBETAN MEDICINE IN SPORTS)." Pedagogical IMAGE 15, no. 1 (2021): 26–37. http://dx.doi.org/10.32343/2409-5052-2021-15-1-26-37.

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Introduction. The study aimed to theoretically substantiate and experimentally test the effectiveness of registering peripheral pulse of freestyle wrestlers, student members of the Russian national team, using the computer-aided pulse diagnosis system. Materials and methods: The paper presents for the first time the results of experimental studies into the heart rate variability with respect to pulse wave during exercise tests using spectral methods, as well as the effect of physical activity on twelve internal organs of an athlete. Research results: The findings indicate the most characterist

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D’Antoni, Federico, Fabrizio Russo, Luca Ambrosio, et al. "Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review." International Journal of Environmental Research and Public Health 19, no. 10 (2022): 5971. http://dx.doi.org/10.3390/ijerph19105971.

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Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically revi

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Reiter, Alisa Maria Vittoria, Jean Tori Pantel, Magdalena Danyel, Denise Horn, Claus-Eric Ott, and Martin Atta Mensah. "Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study." Journal of Medical Internet Research 26 (March 13, 2024): e42904. http://dx.doi.org/10.2196/42904.

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Background While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. Objective We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. Methods Using a retrospective sample

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Noor Najah Ali, Aseel Hameed, Asanka G. Perera, and Ali Al Naji. "Custom YOLO Object Detection Model for COVID-19 Diagnosis." Journal of Techniques 5, no. 3 (2023): 92–100. http://dx.doi.org/10.51173/jt.v5i3.1174.

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The emergence and spread of the new coronavirus (COVID-19) poses a new public health threat to the entire world (SARS-CoV-2). This new virus is highly contagious and pathogenetically different from other mainstream respiratory viruses. Clinical staff can benefit from Computer Aided Diagnostics (CAD) systems that combine deep learning algorithms and image processing technologies as diagnostic tools for COVID-19. These tools also help to better understand the course of the disease. In most cases, medical staff and healthcare facilities would be more equipped to promptly diagnose COVID-19 for pat

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Termine, Andrea, Carlo Fabrizio, Carlo Caltagirone, Laura Petrosini, and on behalf of the Frontotemporal Lobar Degeneration Neuroimaging Initiative. "A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks." Life 12, no. 7 (2022): 947. http://dx.doi.org/10.3390/life12070947.

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Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorit

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R., Lavanya, and K. Rajini G. "Fundus Image Analysis to Detect Abnormalities in Diabetic Retinopathy using Computer Aided Design Tools - A Review." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 224–32. https://doi.org/10.35940/ijeat.C6366.089620.

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Diabetic retinopathy is becoming a major threat to visual loss in human beings. Many researchers are working to develop early detection techniques, which may reduce the risk of vision loss using image-processing techniques like image enhancement and segmentation. Improving the quality of medical images to detect the disease at an early stage is crucial for further medication. It is gaining more focus with automated techniques for machine learning. Filtering and morphological operators enhance image contrast and interested region can be extracted using segmentation techniques from the fundus im

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Owais, Muhammad, Muhammad Arsalan, Tahir Mahmood, Yu Hwan Kim, and Kang Ryoung Park. "Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study." JMIR Medical Informatics 8, no. 12 (2020): e21790. http://dx.doi.org/10.2196/21790.

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Background Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only class

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Shaikh, Imran, and Kadam V.K. "Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection using CNN LSTM." International Journal of Engineering Research in Electronics and Communication Engineering 9, no. 2 (2022): 1–8. http://dx.doi.org/10.36647/ijerece/09.02.a001.

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Liver cancer detection using the computer vision methods and machine learning already received significant attention of researchers for authentic diagnosis and on-time medical attentions. The Computer Aided Diagnosis (CAD) preferred for cancer detection all over the world which is based on image processing service. Earlier CAD tools were designed using conventional machine learning techniue using semi-automatic approach. The modern growth of deep learning for automatic detection and classification leads to significant improvement in accuracy. This paper the automatic CAD framework for liver ca

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Yang, Huan, and Pengjiang Qian. "GAN-Based Medical Images Synthesis." International Journal of Health Systems and Translational Medicine 1, no. 2 (2021): 1–9. http://dx.doi.org/10.4018/ijhstm.2021070101.

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Medical images have always occupied a very important position in modern medical diagnosis. They are standard tools for doctors to carry out clinical diagnosis. However, nowadays, most clinical diagnosis relies on the doctors' professional knowledge and personal experience, which can be easily affected by many factors. In order to reduce the diagnosis errors caused by human subjective differences and improve the accuracy and reliability of the diagnosis results, a practical and reliable method is to use artificial intelligence technology to assist computer-aided diagnosis (CAD). With the help o

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Alçın, Adem, and Asıf Yıldırım. "Artificial Intelligence in Prostate Cancer Diagnosis." New Journal of Urology 19, no. 3 (2024): 151–56. http://dx.doi.org/10.33719/nju1557986.

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Prostate cancer (PCa) is a cancer with a broad spectrum of biological behavior and it is a heterogeneous nature. In order to prevent overdiagnosis and overtreatment, and to detect clinically significant PCa, standardized scoring and grading systems are used in imaging and pathological examinations. However, reproducibility and agreement between readers in these diagnostic stages, which require experience, are low. Promising results have been achieved by integrating artificial intelligence (AI)-based applications into the diagnosis and management of PCa. In radiological and pathological imaging

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Martínez-Murcia, F. J., J. M. Górriz, J. Ramírez, C. G. Puntonet, and D. Salas-González. "Computer Aided Diagnosis tool for Alzheimer’s Disease based on Mann–Whitney–Wilcoxon U-Test." Expert Systems with Applications 39, no. 10 (2012): 9676–85. http://dx.doi.org/10.1016/j.eswa.2012.02.153.

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Żabińska, Iwona, Artur Kuboszek, Erika Sujova, and Jan Zitnansky. "Ergonomic Diagnosis of a Computer Workstation." Multidisciplinary Aspects of Production Engineering 1, no. 1 (2018): 739–44. http://dx.doi.org/10.2478/mape-2018-0093.

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Abstract The article presents the results of research carried out as a part of a project aimed at integrated ergonomic diagnosis of the work environment in terms of improvement of technical and psychosocial conditions. The research carried out so far included small and medium-sized enterprises located in the Śląskie Voivodeship. The tests included blue-collar workers as well as administrative (white-collar) workers. Ergonomic diagnosis was carried out by direct observation of employees at the workplace using tools such as the Ergonomic Control Test CET II and the Dortmund list. This article pr

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Tellakula, KK Praneeth, Saravana Kumar R, and Sanjoy Deb. "A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS." Applied Computer Science 17, no. 2 (2021): 40–55. http://dx.doi.org/10.35784/acs-2021-12.

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The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Appl

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Maiti, Ananjan, Biswajoy Chatterjee, and K. C. Santosh. "Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network." International Journal of Ambient Computing and Intelligence 12, no. 3 (2021): 75–97. http://dx.doi.org/10.4018/ijaci.2021070104.

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Early interpretation of skin cancer through computer-aided diagnosis (CAD) tools reduced the intricacy of the treatments as it can attain a 95% recovery rate. To frame up with computer-aided diagnosis system, scientists adopted various artificial intelligence (AI) designed to receive the best classifiers among these diverse features. This investigation covers traditional color-based texture, shape, and statistical features of melanoma skin lesion and contrasted with suggested methods and approaches. The quantized color feature set of 4992 traits were pre-processed before training the model. Th

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Ziyad, Shabana R., Radha V., and Thavavel Vaiyapuri. "Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique." International Journal of E-Health and Medical Communications 12, no. 5 (2021): 1–15. http://dx.doi.org/10.4018/ijehmc.20210901.oa1.

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Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the su

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Kim, Eun Young, and Myung Jin Chung. "Application of artificial intelligence in chest imaging for COVID-19." Journal of the Korean Medical Association 64, no. 10 (2021): 664–70. http://dx.doi.org/10.5124/jkma.2021.64.10.664.

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Background: The coronavirus disease 2019 (COVID-19) pandemic has threatened public health. Medical imaging tools such as chest X-ray and computed tomography (CT) play an essential role in the global fight against COVID-19. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical professionals. We reviewed the current progress in the development of AI technologies for the diagnostic imaging of COVID-19.Current Concepts: The rapid development of AI, including deep learning, has led to the development of technologies that may assis

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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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Elzeki, Omar M., Mahmoud Shams, Shahenda Sarhan, Mohamed Abd Elfattah, and Aboul Ella Hassanien. "COVID-19: a new deep learning computer-aided model for classification." PeerJ Computer Science 7 (February 18, 2021): e358. http://dx.doi.org/10.7717/peerj-cs.358.

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Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images

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Kanta Maitra, Indra, and Samir Kumar Bandyopadhyay. "CAD Based Method for Detection of Breast Cancer." Oriental journal of computer science and technology 11, no. 3 (2018): 154–68. http://dx.doi.org/10.13005/ojcst11.03.04.

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Breast cancer affecting the women is known to cause high mortality unless detected in right time. Detection requires Mammography followed by biopsy of the tumour or lesions present in the breast tissue. Contemporary Mammographic hardware has incorporated digitization of output imagesfor increasing the scope for implementation of computational methods towards Computer Aided Diagnostics (CAD).CAD systems require Medical Image Processing, a multi-disciplinary science that involves development of computational algorithms on medical images. Histopathological slides are examined for determination of

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Maqsood, Sarmad, Robertas Damaševičius, and Rytis Maskeliūnas. "TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages." Applied Sciences 12, no. 7 (2022): 3273. http://dx.doi.org/10.3390/app12073273.

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Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can

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Tuncer, Seda Arslan, Ahmet Çınar, and Murat Fırat. "Hybrid CNN Based Computer-Aided Diagnosis System for Choroidal Neovascularization, Diabetic Macular Edema, Drusen Disease Detection from OCT Images." Traitement du Signal 38, no. 3 (2021): 673–79. http://dx.doi.org/10.18280/ts.380314.

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In the treatment of eye diseases, optical coherence tomography (OCT) is a medical imaging method that displays biological tissue layers by taking high resolution tomographic sections at the micron level. It has an important role in the diagnosis and follow-up of many diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), age-related macular degeneration (AMD), Diabetic Retinopathy, Central Serous Retinopathy, Epiretinal Membrane, and Macular Hole. Computer-Aided Diagnostic (CAD) tools are needed in early detection and treatment monitoring of such eye diseases. In th

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Adarkar, Darshan, Atharva Lokapur, Janhavi Porwal, and Pratik Mali. "Chronic Kidney Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4239–43. http://dx.doi.org/10.22214/ijraset.2023.51239.

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Abstract: Kidney stones play a role in the development of chronic kidney disease. Recurrent kidney stones should be avoided not only because of their immediate clinical manifestations but also because of their long-term predisposition to CKD progression. A lot of people confess to emergency departments with excruciating pain due to kidney stones, which are prevalent ailments around the world. The diagnosis of kidney stone illness involves the use of many imaging modalities. For the entire diagnosis and interpretation of these photos, specialists are required. Systems for computer-aided diagnos

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Marten, K., V. Dicken, C. Kneitz, et al. "Interstitial lung disease associated with collagen vascular disorders: disease quantification using a computer-aided diagnosis tool." European Radiology 19, no. 2 (2008): 324–32. http://dx.doi.org/10.1007/s00330-008-1152-1.

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Touati, Haifa, Areej Alasiry, Abdulmajid Al-Junaid, et al. "Contribution to an Advanced Clinical Aided Tool Dedicated to Explore ASPECTS Score of Ischemic Stroke." Journal of Image and Graphics 12, no. 1 (2024): 40–52. http://dx.doi.org/10.18178/joig.12.1.40-52.

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The Alberta Stroke Program Early CT Score (ASPECTS) is a simple and reliable systematic method used to quantify and explore acute ischemic stroke. It was initially developed to standardize the assessment of the early ischemic changes’ extent within the Middle Cerebral Artery (MCA). The ASPECTS assessment has been increasingly incorporated into treatment decision-making and has been used in several randomized clinical trials for endovascular treatment decision-making. The e-ASPECTS software is a tool for the automated use of ASPECTS. The purpose of this paper is twofold: The first objective is

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Thilagavathi, J., Semil P, S. Sudhanthirapriya, and T. Sargunam. "Multiple Eye Diseases Detection using Convolutional Neural Network." Shanlax International Journal of Arts, Science and Humanities 11, S3-July (2024): 49–52. http://dx.doi.org/10.34293/sijash.v11is3-july.7918.

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Many eye cases that will arise in the next few years will require early diagnosis for rapid intervention. Polyophthalmia diagnostic methods such as physical examination, examination and diagnosis will be limited to medical and professional methods. Therefore, automated processes are needed. There have been some studies on computer-aided diagnosis (CAD) of polyocular disease using tools such as experts, but these are limited to their knowledge base and therefore not accurate. Early diagnosis of polyophthalmia allows rapid intervention and treatment. This application uses convolutional neural ne

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Yan Huan, Ch’ng, Mohd Azam Osman, and Jong Hui Ying. "An Innovation-Driven Approach to Specific Language Impairment Diagnosis." Malaysian Journal of Medical Sciences 28, no. 2 (2021): 161–70. http://dx.doi.org/10.21315/mjms2021.28.2.15.

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Background: Specific language impairment (SLI) diagnosis is inconvenient due to manual procedures and hardware cost. Computer-aided SLI diagnosis has been proposed to counter these inconveniences. This study focuses on evaluating the feasibility of computer systems used to diagnose SLI. Methods: The accuracy of Webgazer.js for software-based gaze tracking is tested under different lighting conditions. Predefined time delays of a prototype diagnosis task automation script are contrasted against with manual delays based on human time estimation to understand how automation influences diagnosis a

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D Bonde, Girish, and Dr Manish Jain. "Analysis of MRI Data of Brain for CAD System." International Journal of Engineering & Technology 7, no. 2.17 (2018): 63. http://dx.doi.org/10.14419/ijet.v7i2.17.11560.

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Magnetic resonance imaging (MRI) technologies are currently one of the most effective tools in the diagnosis of a wide variety of socially significant pathologies including cancer, arteriosclerosis, episodes. Ischemic and neurodegenerative diseases [1, 2, 3, 4].This paper gives detailed idea of pre-processing, and segmentation(FCM, soft and hard) of MRI brain tumor images. This paper also insights the machine learning(SOM, NN and SVM) approach for automatic classification(PTPSA, fBM) of brain tissues. Different performance evaluation parameter and similarity metrics are discuss to define the e

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Sharma, Vandana, and Divya Midhunchakkaravarthy. "Local post-hoc interpretable machine learning model for prediction of dementia in young adults." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 3 (2023): 1569. http://dx.doi.org/10.11591/ijeecs.v32.i3.pp1569-1579.

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<p>Dementia is still the prevailing brain disease with late diagnosis. There is a large increase in dementia disease among young adults. The major reason is over indulgence of young adults on social media resulting in denial of disease and delayed clinical diagnosis. Dementia is preventable and curable if diagnosed at an early stage, however, no attempts are being made to miti gate dementia in young adults. Today artificial intelligence (AI) based advanced technology with real-life consultations in clinical or remote setups are proved beneficial and is used to detect dementia. Most AI-ba

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Loh, Hui Wen, Wanrong Hong, Chui Ping Ooi, et al. "Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)." Sensors 21, no. 21 (2021): 7034. http://dx.doi.org/10.3390/s21217034.

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Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease

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Alves, Rui, Marc Piñol, Jordi Vilaplana, et al. "Computer-assisted initial diagnosis of rare diseases." PeerJ 4 (July 21, 2016): e2211. http://dx.doi.org/10.7717/peerj.2211.

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Introduction.Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Disco

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