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Journal articles on the topic 'Automatic diagnosis'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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1

Palla, Gabriella, Claudio Ughi, Graziano Cesaretti, Alessandro Ventura, and Giuseppe Maggiore. "“Automatic” diagnosis ofviral enteritis." Journal of Pediatrics 130, no. 6 (1997): 1013. http://dx.doi.org/10.1016/s0022-3476(97)70302-0.

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2

Masanov, D. V., and L. P. Sebina. "Automatic Diagnosis of Texturing." Fibre Chemistry 37, no. 2 (2005): 105–8. http://dx.doi.org/10.1007/s10692-005-0064-y.

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3

Chen, Junying, Dongfang Li, Qingcai Chen, Wenxiu Zhou, and Xin Liu. "Diaformer: Automatic Diagnosis via Symptoms Sequence Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4432–40. http://dx.doi.org/10.1609/aaai.v36i4.20365.

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Automatic diagnosis has attracted increasing attention but remains challenging due to multi-step reasoning. Recent works usually address it by reinforcement learning methods. However, these methods show low efficiency and require task-specific reward functions. Considering the conversation between doctor and patient allows doctors to probe for symptoms and make diagnoses, the diagnosis process can be naturally seen as the generation of a sequence including symptoms and diagnoses. Inspired by this, we reformulate automatic diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but effective automatic Diagnosis model based on Transformer (Diaformer). We firstly design the symptom attention framework to learn the generation of symptom inquiry and the disease diagnosis. To alleviate the discrepancy between sequential generation and disorder of implicit symptoms, we further design three orderless training mechanisms. Experiments on three public datasets show that our model outperforms baselines on disease diagnosis by 1%, 6% and 11.5% with the highest training efficiency. Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.

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4

Kasperczuk, Anna, and Agnieszka Dardzinska. "Automatic system for IBD diagnosis." Procedia Computer Science 192 (2021): 2863–70. http://dx.doi.org/10.1016/j.procs.2021.09.057.

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5

Zhang, XueNong, and Wei Lu. "Automatic Diagnosis with Constraint Solver." International Journal of Information and Computer Science 4 (2015): 30. http://dx.doi.org/10.14355/ijics.2015.04.005.

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6

ABDUL-SADA, JAFAR W., and H. J. ABBAS. "Automatic diagnosis from electrocardiograms (ECGs)." International Journal of Systems Science 19, no. 11 (1988): 2157–62. http://dx.doi.org/10.1080/00207728808964108.

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Mossin, Eduardo André, Dennis Brandão, Guilherme Serpa Sestito, and Renato Veiga Torres. "Automatic Diagnosis for Profibus Networks." Journal of Control, Automation and Electrical Systems 27, no. 6 (2016): 658–69. http://dx.doi.org/10.1007/s40313-016-0261-3.

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8

Nachlieli, Hila, Zachi Karni, and Shaul Raz. "Perception Guided Automatic Press Diagnosis." NIP & Digital Fabrication Conference 27, no. 1 (2011): 784–87. http://dx.doi.org/10.2352/issn.2169-4451.2011.27.1.art00096_2.

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9

Kojima, Tomoyuki, and Masahiro Kato. "Algorithm for Automatic Diagnosis of Aphasia." Higher Brain Function Research 16, no. 3 (1996): 221–26. http://dx.doi.org/10.2496/apr.16.221.

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NAKAYAMA, Keizo. "Trends in automatic cytological diagnosis screening in Japan - Evaluation of automatic cytological diagnosis screening supporting AutoPap." Journal of the Japanese Society of Clinical Cytology 40, no. 2 (2001): 204–10. http://dx.doi.org/10.5795/jjscc.40.204.

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Zhang, Fuxi, Guoming Sang, Zhi Liu, Hongfei Lin, and Yijia Zhang. "A doctor’s diagnosis experience enhanced transformer model for automatic diagnosis." Engineering Applications of Artificial Intelligence 134 (August 2024): 108675. http://dx.doi.org/10.1016/j.engappai.2024.108675.

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Suryadevara, Gnanitha. "Automated Malaria Diagnosis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41123.

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- Early and accurate diagnosis of malaria is important for effective treatment and reduction of mortality rates. Traditional microscopy and manual cell counting methods are labor-intensive and prone to errors. This project introduces an innovative solution in the form of an automated cell counting system. The system is designed to efficiently identify and count red blood cells (RBCs), distinguishing between uninfected and malaria-infected cells with high precision. The proposed system will overcome the limitations of conventional methods by automating the counting process, with reduced processing time and minimized human error. The approach promises to deliver faster and more accurate diagnostic reports, which would facilitate early detection and treatment of malaria. The efficiency of the model will be judged based on the precision in the identification and enumeration of cells as well as its capability to provide diagnostic reports with a speed far more superior than that of the manual procedures. Finally, this automatic system will lead to an efficient diagnosis, improved patient care, and a further contribution to the fight against malaria. Key Words: Automated Diagnosis, Malaria, Red Blood Cell Counting, CNN, Deep Learning, VGG16.

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13

Les, Tomasz, Tomasz Markiewicz, Miroslaw Dziekiewicz, and Malgorzata Lorent. "Kidney Boundary Detection Algorithm Based on Extended Maxima Transformations for Computed Tomography Diagnosis." Applied Sciences 10, no. 21 (2020): 7512. http://dx.doi.org/10.3390/app10217512.

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This article describes the automated computed tomography (CT) image processing technique supporting kidney detection. The main goal of the study is a fully automatic generation of a kidney boundary for each slice in the set of slices obtained in the computed tomography examination. This work describes three main tasks in the process of automatic kidney identification: the initial location of the kidneys using the U-Net convolutional neural network, the generation of an accurate kidney boundary using extended maxima transformation, and the application of the slice scanning algorithm supporting the process of generating the result for the next slice, using the result of the previous one. To assess the quality of the proposed technique of medical image analysis, automatic numerical tests were performed. In the test section, we presented numerical results, calculating the F1-score of kidney boundary detection by an automatic system, compared to the kidneys boundaries manually generated by a human expert from a medical center. The influence of the use of U-Net support in the initial detection of the kidney on the final F1-score of generating the kidney outline was also evaluated. The F1-score achieved by the automated system is 84% ± 10% for the system without U-Net support and 89% ± 9% for the system with U-Net support. Performance tests show that the presented technique can generate the kidney boundary up to 3 times faster than raw U-Net-based network. The proposed kidney recognition system can be successfully used in systems that require a very fast image processing time. The measurable effect of the developed techniques is a practical help for doctors, specialists from medical centers dealing with the analysis and description of medical image data.

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Martinho, Rita, Jéssica Lopes, Diogo Jorge, Luís Caldas de Oliveira, Carlos Henriques, and Paulo Peças. "IoT Based Automatic Diagnosis for Continuous Improvement." Sustainability 14, no. 15 (2022): 9687. http://dx.doi.org/10.3390/su14159687.

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This work responds to the gap in integrating the Internet-of-Things in Continuous Improvement processes, especially to facilitate diagnosis and problem-solving activities regarding manufacturing workstations. An innovative approach, named Automatic Detailed Diagnosis (ADD), is proposed: a non-intrusive, easy-to-install and use, low-cost and flexible system based on industrial Internet-of-Things platforms and devices. The ADD requirements and architecture were systematized from the Continuous Improvement knowledge field, and with the help of Lean Manufacturing professionals. The developed ADD concept is composed of a network of low-power devices with a variety of sensors. Colored light and vibration sensors are used to monitor equipment status, and Bluetooth low-energy and time-of-flight sensors monitor operators’ movements and tasks. A cloud-based platform receives and stores the collected data. That information is retrieved by an application that builds a detailed report on operator–machine interaction. The ADD prototype was tested in a case study carried out in a mold-making company. The ADD was able to detect time performance with an accuracy between 89% and 96%, involving uptime, micro-stops, and setups. In addition, these states were correlated with the operators’ movements and actions.

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Avanzato, Roberta, and Francesco Beritelli. "Automatic ECG Diagnosis Using Convolutional Neural Network." Electronics 9, no. 6 (2020): 951. http://dx.doi.org/10.3390/electronics9060951.

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Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases.

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16

Sun, Qiao, Ping Chen, Dajun Zhang, and Fengfeng Xi. "Pattern Recognition for Automatic Machinery Fault Diagnosis." Journal of Vibration and Acoustics 126, no. 2 (2004): 307–16. http://dx.doi.org/10.1115/1.1687391.

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We present a generic methodology for machinery fault diagnosis through pattern recognition techniques. The proposed method has the advantage of dealing with complicated signatures, such as those present in the vibration signals of rolling element bearings with and without defects. The signature varies with the location and severity of bearing defects, load and speed of the shaft, and different bearing housing structures. More specifically, the proposed technique contains effective feature extraction, good learning ability, reliable feature fusion, and a simple classification algorithm. Examples with experimental testing data were used to illustrate the idea and effectiveness of the proposed method.

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17

Nunes, Luciano Comin, Placido Rogerio Pinheiro, Mirian Caliope Dantas Pinheiro, Marum Simao Filho, Rafael Espindola Comin Nunes, and Pedro Gabriel Caliope Dantas Pinheiro. "Automatic Detection and Diagnosis of Neurologic Diseases." IEEE Access 7 (2019): 29924–41. http://dx.doi.org/10.1109/access.2019.2899216.

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18

Sharan, Roneel V., Udantha R. Abeyratne, Vinayak R. Swarnkar, and Paul Porter. "Automatic Croup Diagnosis Using Cough Sound Recognition." IEEE Transactions on Biomedical Engineering 66, no. 2 (2019): 485–95. http://dx.doi.org/10.1109/tbme.2018.2849502.

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19

Liu, Jiang, Zhuo Zhang, Damon Wing Kee Wong, et al. "Automatic glaucoma diagnosis through medical imaging informatics." Journal of the American Medical Informatics Association 20, no. 6 (2013): 1021–27. http://dx.doi.org/10.1136/amiajnl-2012-001336.

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20

VINK, J. P., M. LAUBSCHER, R. VLUTTERS, et al. "An automatic vision-based malaria diagnosis system." Journal of Microscopy 250, no. 3 (2013): 166–78. http://dx.doi.org/10.1111/jmi.12032.

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21

Fernandez-Granero, M. A., A. Sarmiento, D. Sanchez-Morillo, S. Jiménez, P. Alemany, and I. Fondón. "Automatic CDR Estimation for Early Glaucoma Diagnosis." Journal of Healthcare Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5953621.

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Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L∗a∗b∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L∗a∗b∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.

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22

Vinson, J. M., S. D. Grantham, and L. H. Ungar. "Automatic rebuilding of qualitative models for diagnosis." IEEE Expert 7, no. 4 (1992): 23–30. http://dx.doi.org/10.1109/64.153461.

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23

Arshad, Junaid, Paul Townend, and Jie Xu. "An automatic intrusion diagnosis approach for clouds." International Journal of Automation and Computing 8, no. 3 (2011): 286–96. http://dx.doi.org/10.1007/s11633-011-0584-2.

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24

RRadheesha, Mrs. "Fault diagnosis using automatic test packet generation." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 919–22. http://dx.doi.org/10.17762/ijritcc2321-8169.150304.

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25

Balado, J., L. Díaz-Vilariño, P. Arias, and M. Soilán. "Automatic building accessibility diagnosis from point clouds." Automation in Construction 82 (October 2017): 103–11. http://dx.doi.org/10.1016/j.autcon.2017.06.026.

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26

Heinitz, Matthias, Ingo Hollenbeck, Martin Kuboschek, Jan Otterstedt, Christian Sebeke, and Thomas Winkel. "Automatic fault diagnosis for scan-based designs." Microelectronic Engineering 31, no. 1-4 (1996): 331–38. http://dx.doi.org/10.1016/0167-9317(95)00355-x.

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27

Li, L., and A. D. Malony. "Knowledge engineering for automatic parallel performance diagnosis." Concurrency and Computation: Practice and Experience 19, no. 11 (2007): 1497–515. http://dx.doi.org/10.1002/cpe.1127.

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28

Gouskir, Mohamed, Belaid Bouikhalene, Hicham Aissaoui, and Benachir Elhadadi. "Automatic Diagnosis of Brain Magnetic Resonance Images based on Riemannian Geometry." Journal of Electronic Commerce in Organizations 13, no. 2 (2015): 30–40. http://dx.doi.org/10.4018/jeco.2015040103.

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Automated brain tumor detection and segmentation, from medical images, is one of the most challenging. The authors present, in this paper, an automatic diagnosis of brain magnetic resonance image. The goal is to prepare the image of the human brain to locate the existence of abnormal tissues in multi-modal brain magnetic resonance images. The authors start from the image acquisition, reduce information, brain extraction, and then brain region diagnosis. Brain extraction is the most important preprocessing step for automatic brain image analysis. The authors consider the image as residing in a Riemannian space and they based on Riemannian manifold to develop an algorithm to extract brain regions, these regions used in other algorithm to brain tumor detection, segmentation and classification. Riemannian Manifolds show the efficient results to brain extraction and brain analysis for multi-modal resonance magnetic images.

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Peng, Bin. "Qualitative Diagnosis of Liver Tumors Based on Ultrasound-Guided Automatic Biopsy." Journal of Healthcare Engineering 2021 (November 18, 2021): 1–12. http://dx.doi.org/10.1155/2021/8585887.

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The goal of this study was to see if automatic biopsy guided by ultrasound could be used to provide a qualitative diagnosis of a liver tumor. Methods. A total of 40 patients (101 focuses) were treated with automatic liver parenchyma biopsy under ultrasound guidance, and the correlation between pathological outcomes and ultrasound images was investigated. The lesion size in the observation group was compared to that in the control group using conventional ultrasound ( P > 0.05), and there was no significant difference. Under contrast-enhanced ultrasound (CEUS), there was no statistically significant difference in lesion size between the observation and control groups ( P > 0.05). The difference in lesion size between the conventional ultrasonography and CEUS observation groups was statistically significant ( P 0.05). Conclusion. Ultrasound-guided automated biopsy of the liver parenchyma is a simple and effective procedure with fewer problems and a high diagnostic rate, and it deserves to be promoted clinically.

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Liu, Ningtao, Jie Du, Shiliang Chang, et al. "An automatic diagnosis method of power consumption anomaly of station users based on the k-medoids clustering algorithm." Journal of Physics: Conference Series 2781, no. 1 (2024): 012032. http://dx.doi.org/10.1088/1742-6596/2781/1/012032.

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Abstract Obtaining reliable data on electricity consumption can be difficult due to faulty or inaccurate data acquisition equipment. Therefore, a k-medoids clustering algorithm is used to design an automatic diagnosis method of power consumption anomaly. The K-Medoids algorithm was used to cluster the power consumption data of users in the Taiwan area. The data dimensions suitable for automatic diagnosis are screened by the ADF method. Based on this, the power consumption anomaly of the distribution network station area is automatically diagnosed, and the marked power consumption behavior data characteristics are checked step by step to realize the automatic power consumption anomaly diagnosis of station area users. The experimental results show that the K-medoids clustering algorithm can reasonably avoid the influence of transient abnormal data caused by isolated points on the automatic anomaly diagnosis results. For different types of abnormal automatic diagnosis rate of more than 98.6%, can accurately diagnose the abnormal power consumption of users in the station area.

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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 cancer detection using Convolutional Neural Network (CNN) including Long Short Term Memory (LSTM). The input Computed Tomography (CT) scan images early pre-processed for quality enhancement. After that we applied the lightweight and accuracy field of Interest (ROI) extraction technique using dynamic binary segmentation. From ROI images, we extracted automated CNN-based appearance and hand-craft features. The consolidation of both features formed unique feature set for classification purpose. The LSTM block is then achieve the classification either into normal or diseased CT image. The CNN-LSTM model is designed in this paper to complement the accuracy of liver cancer detection compared to other deep learning solutions. The experimental results of proposed design using CNN-based features and hybrid hand craft features outperformed the recent state-of-art methods.

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32

Sihombing, Darvin Markus Pardamean, and Arjon Samuel Sitio. "Penerapan Sistem Pakar Mendiagnosa Kerusakan Sepeda Motor Automatic dan Injeksi Berbasis Android Dengan Metode Forward Chaining." Jurnal Sistem Informasi Kaputama (JSIK ) 5, no. 2 (2021): 106–14. http://dx.doi.org/10.59697/jsik.v5i2.701.

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Sepeda motor sebagai salah satu alat transportasi yang banyak di sukai oleh masyarakat Indonesia, oleh karena itu pengetahuan mengenai sepeda motor khususnya jika ada kerusakan perlu dikuasai oleh penggunanya. sistem yang di kembangkan ini untuk mendiagnosis kerusakan sepeda motor disebut dengan sistem pakar diagnosis kerusakan sepeda motor automatic dan injeksi. Tujuan penelitian ini adalah mengembangkan sistem pakar diagnosis kerusakan sepeda motor automatic dan injeksi dimana pengembangannya menggunakan metode Forward Chaining dimana tahapan yang dilakukan dengan menggunakan basis data dan rule - rule atau kode tertentu untuk membangun basis pengetahuan dalam bentuk aturan yang digunakan dalam mendiagnosis kerusakan sepeda motor automatic dan injeksi. Tahapan metode dimulai dari assessment, akuisisi pengetahuan, desain, pengujian, dokumentasi dan pemeliharaan. Berdasarkan tahapan yang telah dilakukan maka diperoleh suatu prototype sistem pakar diagnosis kerusakan sepeda motor automatic dan injeksi dengan menggunakan bahasa pemograman android studio. Sistem pakar ini menyediakan fasilitas berupa halaman yang berisi tentang sistem pakar diagnosis kerusakan sepeda motor automatic dan injeksi, kemudian halaman daftar kerusakan, kemudian pengguna bisa melakukan konsultasi mengenai kerusakan sepeda motor automatic dan injeksi sesuai dengan gejalanya, sehingga sistem akan menampilkan hasil mendiagnosis kerusakan sepeda motor automatic dan injeksi berupa nama kerusakan kendaraan beserta solusinya.

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33

Arce-Lopera, Carlos Alberto, Javier Diaz-Cely, and Lina Quintero. "Presumptive Diagnosis of Cutaneous Leishmaniasis." Frontiers in Health Informatics 10, no. 1 (2021): 75. http://dx.doi.org/10.30699/fhi.v10i1.278.

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Introduction: Cutaneous Leishmaniasis is a neglected tropical disease caused by a parasite. The most common presumptive diagnostic tool for this disease is the visual examination of the associated skin lesions by medical experts. Here, a mobile application was developed to aid this pre-diagnosis using an automatic image recognition software based on a convolutional neural network model.Material and Methods: A total of 2022 images of cutaneous diseases taken from 2012 to 2018 were used for training. Then, in 2019, machine learning techniques were tested to develop an automatic classification model. Also, a mobile application was developed and tested against specialized human experts to compare its performance.Results: Transfer learning using the VGG19 model resulted in a 93% accuracy of the classification model. Moreover, on average, the automatic model performance on a randomly selected skin image sample revealed a 99% accuracy while, the ensemble prediction of seven human medical expert’s accuracy was 83%.Conclusion: Mobile skin monitoring applications are crucial developments for democratizing health access, especially for neglected tropical diseases. Our results revealed that the image recognition software outperforms human medical experts and can alert possible patients. Future developments of the mobile application will focus on health monitoring of Cutaneous Leishmaniasis patients via community leaders and aiming at the promotion of treatment adherence.

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Korobova, A. G., S. Yu Meshchurova, E. E. Trushina, and L. M. Samokhodskaya. "Experience with the use of an automated system for diagnosis of urinary tract infections." Clinical Microbiology and Antimicrobial Chemotherapy 25, no. 4 (2023): 408–14. http://dx.doi.org/10.36488/cmac.2023.4.408-414.

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Objective. To compare results of microbiological examination obtained on rapid automated system with semiquantitative plate culture to assess possibility and necessity of using the system in urine examination. Materials and Methods. The study included 231 urine samples collected from February to July 2023 from patients at Lomonosov Moscow State University Medical Research and Educational Center. The samples were cultured according to the standards of urine microbiological examination using chromogenic media and using an automatic HB&L system (Alifax, Italy) for 4 h. 30 min. to detect bacteriuria of 102 CFU/ ml or more and residual antimicrobial activity. When microbial growth was detected in the analyzer, extraction of microorganisms was performed on the same day for accelerated identification. Identification was performed by MALDI-TOF mass spectrometry. Results. According to the plate culture method, 160 positive samples were obtained. A total of 273 isolates were isolated, the predominant microorganisms were E. faecalis (22.3%) and E. coli (19.8%), a significant part was composed of atypical pathogens for UTI and normobiota (33.7%). According to the rapid automated system, only in 100 samples the growth of microorganisms was detected, in 4 cases the positive result obtained by the automatic system was not confirmed by growth on chromogenic media. Thus, 64 of the 160 positive cultures were not detected using the automated system, and 14 of those ones was E. coli. Residual antimicrobial activity was detected in 104 samples, including 43 of 64 false-negative culture results using the automated system. Rapid identification was performed on 57 samples, a microbial identification result was obtained in 49 of them. Complete or partial match between the results of rapid identification and classical methods was obtained for 48 samples. In all cases, when rapid identification was performed for a sample with monoculture, the results of two methods were identical. Conclusions. An overall sensitivity and specificity of the culture method using an automatic system were 60% and 94.4%, respectively. Sensitivity for samples containing E. coli was 74.1%, and for their isolation in monoculture – 87.5%.

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Sun, Jie, Kai Yao, Guangyao Huang, et al. "Machine Learning Methods in Skin Disease Recognition: A Systematic Review." Processes 11, no. 4 (2023): 1003. http://dx.doi.org/10.3390/pr11041003.

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Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due to similar symptoms among certain types of lesions. The motivation for this study is to collate and analyze machine learning (ML) applications in skin lesion research, with the goal of encouraging the development of automated systems for skin disease diagnosis. To assist dermatologists in their clinical diagnosis, several skin image datasets have been developed and published online. Such efforts have motivated researchers and medical staff to develop automatic skin diagnosis systems using image segmentation and classification processes. This paper summarizes the fundamental steps in skin lesion diagnosis based on papers mainly published since 2013. The applications of ML methods (including traditional ML and deep learning (DL)) in skin disease recognition are reviewed based on their contributions, methods, and achieved results. Such technical analysis is beneficial to the continuing development of reliable and effective computer-aided skin disease diagnosis systems. We believe that more research efforts will lead to the current automatic skin diagnosis studies being used in real clinical settings in the near future.

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36

Shaikh, Imran, and Kadam V.K. "Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection with Simulation using CNN LSTM." International Journal of Engineering Research in Electrical and Electronics Engineering 9, no. 1 (2022): 1–7. http://dx.doi.org/10.36647/ijereee/09.01.a001.

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Initial prediction of any kind of cancer is always advantageous for on-time medical treatment to save the patient's life. The Computer-Aided Diagnosis (CAD) tools using signal processing & image processing methods gained significant attention for immediate & accurate diagnosis using patient’s raw medical data like Magnetic Resonance Imaging (MRI), Chromatography (CT), etc. The liver cancer early detection & analysis of its grading is an important research problem. In this research, we proposed the two models semi-automatic & automatic frameworks for liver disease classification. The models perform early detection of liver cancer accurately followed by its grading analysis into different stages like stage 1 (T1), stage 2 (T2), & stage 3 (T3). The proposed framework consists of stages like pre-processing, Region of Interest (ROI) extraction, features extraction, & classification. The raw CT scans of the liver are pre-processed to remove the noises using the filtering & contrast adjustment functions. The adaptive segmentation method is designed to using binarization & morphological operations to extract the accurate ROI with the minimum computational burden. For features extraction, the text features extracted using Gray Level Co-occurrence Matrix (GLCM), shape features using geometric moment, & automatic features using Convolutional Neural Network (CNN). The hybrid form of features normalized using the min-max technique. For the classifications, we explored the classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), & Long Term Short Memory (LSTM). We investigated the semi-automated & automated systems using the publically available research dataset.

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37

Sanchez-Sanchez, Paola A., José Rafael García-González, and Juan Manuel Rúa Ascar. "Automatic migraine classification using artificial neural networks." F1000Research 9 (June 16, 2020): 618. http://dx.doi.org/10.12688/f1000research.23181.1.

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Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

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Sanchez-Sanchez, Paola A., José Rafael García-González, and Juan Manuel Rúa Ascar. "Automatic migraine classification using artificial neural networks." F1000Research 9 (July 17, 2020): 618. http://dx.doi.org/10.12688/f1000research.23181.2.

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Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

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S. Murugappan, Rajeev Kumar Tiwari,. "Reptile Search Algorithm with Deep Convolutional Neural Network for Cloud Assisted Colorectal Cancer Detection and Classification." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 1057–73. http://dx.doi.org/10.52783/tjjpt.v44.i4.970.

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Cloud-based automatic colorectal cancer (CC) detection involves the usage of cloud computing technology and system to help in the earlier and accurate diagnosis of CC in medical images and patient information. This cloud-based technology aims to improve the efficiency and reliability of CC screening, monitoring, and diagnoses. Automatic CC detection refers to the use of computer-based technology and systems to aid in the earlier and accurate detection of CC in patient data and medical images. This automated system aims to increase the reliability and efficiency of CC monitoring, screening, and diagnosis. Deep learning (DL) methods, especially convolutional neural networks (CNNs), exhibit promising results in automatic CC diagnosis. They can be trained on wide-ranging datasets of medical images to learn patterns and features related to precancerous and cancerous lesion. This study develops a new Reptile Search Algorithm with Deep Learning for Colorectal Cancer Detection and Classification (RSADL-CCDC) technique. The main aim of the RSADL-CCDC method focuses on the automaticclassification and recognition of the CC in the cloud environment. Once the medical images are stored in the cloud server, the detection process is carried out. In the presented RSADL-CCDC approach, the initial stage of preprocessing is performed by bilateral filtering (BF) approach. For feature extraction, the RSADL-CCDC technique applies ShuffleNetv2 model. Besides, the recognition and classification of CC take place using convolutional autoencoder (CAE) model. Finally, the hyperparameter tuning of the CAE technique takes place by utilizing RSA. The experimental validation of the RSADL-CCDC system is performed on benchmark medical database. Extensive results stated the enhanced performance of the RSADL-CCDC technique on CC recognition over other models with respect tovarious actions.

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Hu, Wei, Yi Bing Deng, Hong Qi Feng, Qing E. Wu, Bin Tang, and Jian Hua Zou. "A Framework Design of Automatic Fault Diagnosis System." Applied Mechanics and Materials 330 (June 2013): 635–38. http://dx.doi.org/10.4028/www.scientific.net/amm.330.635.

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To resolve a lasting suitable cabin environment for the astronauts, this paper proposes an effective framework design for automatic fault diagnosis system. This framework can implement a real-time online diagnosis and decision support for fault, and carry out an early diagnosis for weak fault. Finally, this paper achieves an online automatic fault diagnosis system by using neural networks self-learning characteristics and expert knowledge. In two-men-two-days simulated manned space flight test, the software of diagnosis system framework worked well, which has been assessed and verified comprehensively.

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Kang, Min-Seok, Deok-Yeong Cheong, Dong-Hee Park, Kyung-Ho Sun, Sang-Hyuk Lee, and Byeong-Keun Choi. "Development of Rule-based Diagnosis for the Automatic Diagnosis of Vertical Pumps." Transactions of the Korean Society for Noise and Vibration Engineering 34, no. 6 (2024): 717–27. https://doi.org/10.5050/ksnve.2024.34.6.717.

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Kuo, Wen-Hung, Shu-Chuan Chuang, Shou-Huan Yang, Ku Yung-Lung, Argon Chen, and King-Jen Chang. "Automatic BI-RADS Diagnosis of Breast Lesions by CAD (Computer-Aid Diagnosis)." Ultrasound in Medicine & Biology 39, no. 5 (2013): S43. http://dx.doi.org/10.1016/j.ultrasmedbio.2013.02.215.

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Abed alelah, Evan, and Babak Karasfi. "Diagnosis of heart disease using clinical data of cardiac patients." Mustansiriyah Journal of Pure and Applied Sciences 3, no. 1 (2025): 144–58. https://doi.org/10.47831/mjpas.v3i1.282.

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Heart disease is a major global health concern and requires early and accurate diagnosis of effective treatment and prevention. Traditional methods often rely on manual interpretation, which can take a long time and be subjective. Automatic learning techniques can improve the accuracy, efficiency and objectivity of cardiology diagnosis by identifying complex data patterns, ensuring objective decision-making and enabling efficient data analysis. Automated learning applications in cardiology diagnosis include risk forecasting, medical imaging diagnosis and clinical decision support systems. It enables early identification of high-risk individuals, accurate interpretation of medical images and real-time clinical guidance. Health-care professionals, through the use of automated learning, can develop personal therapeutic plans that will lead to better outcomes for the patient and promote cardiovascular care.

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MIYAHARA, HIDEO. "Arrhythmia diagnosis performance of an automatic electrocardiogram analyzer." Japanese Journal of Electrocardiology 10, no. 6 (1990): 797–806. http://dx.doi.org/10.5105/jse.10.797.

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Xiufen, Chen. "Automatic zero sequence fault monitoring and diagnosis analysis." Research on Smart Grid 2, no. 1 (2020): 8–16. http://dx.doi.org/10.35534/rsg.0201002c.

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Lee, Haiyoung. "An Automatic Diagnosis Technique of Looper System Response." Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 36, no. 9 (2022): 43–48. http://dx.doi.org/10.5207/jieie.2022.36.9.043.

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Xu, Jing. "Rule-based automatic software performance diagnosis and improvement." Performance Evaluation 67, no. 8 (2010): 585–611. http://dx.doi.org/10.1016/j.peva.2009.07.004.

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Xu, Jing. "Rule-based automatic software performance diagnosis and improvement." Performance Evaluation 69, no. 11 (2012): 525–50. http://dx.doi.org/10.1016/j.peva.2009.11.003.

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Moon, Woo Kyung. "0647: Automatic Breast Ultrasound and Computer Aided Diagnosis." Ultrasound in Medicine & Biology 35, no. 8 (2009): S88. http://dx.doi.org/10.1016/j.ultrasmedbio.2009.06.1049.

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Arzbaecher, Robert. "Automatic diagnosis of atrial fibrillation in implanted devices." Journal of Electrocardiology 24 (January 1991): 134. http://dx.doi.org/10.1016/s0022-0736(10)80034-2.

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