Relevant bibliographies by topics / Disease classification model

Contents

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

Academic literature on the topic 'Disease classification model'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Disease classification model"

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Krishnaprasath, V. T., and J. Preethi. "Finite automata model for leaf disease classification." Agricultural Economics (Zemědělská ekonomika) 67, No. 6 (2021): 220–26. http://dx.doi.org/10.17221/70/2020-agricecon.

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In this modern era, the detection of plant disease plays a vital role in the sustainability of agricultural ecosystem. Today, India being second in farming, well-timed information related to crop is still questioning. Indian Government's farmer portal is available for pesticides, fertilisers, and farm machinery. To alleviate this problem, the paper describes a model to validate the leaf image, predicting leaf disease and notifying the farmer in an effective way on the harvest failure to stabilise farming income. For specific consideration on the validation, a data set library with predefined,
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Sairam Naidu, Chaitanya. "Machine Learning Model for Stroke Disease Classification." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 584–87. http://dx.doi.org/10.21275/sr221012162112.

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Wahab Sait, Abdul Rahaman. "Artificial Intelligence-Driven Eye Disease Classification Model." Applied Sciences 13, no. 20 (2023): 11437. http://dx.doi.org/10.3390/app132011437.

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Eye diseases can result in various challenges and visual impairments. These diseases can affect an individual’s quality of life and general health and well-being. The symptoms of eye diseases vary widely depending on the nature and severity of the disease. Early diagnosis can protect individuals from visual impairment. Artificial intelligence (AI)-based eye disease classification (EDC) assists physicians in providing effective patient services. However, the complexities of the fundus image affect the classifier’s performance. There is a demand for a practical EDC for identifying eye diseases i
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Kumar Agrawal, Tarun, and Dharmendra Gupta. "Heart Disease Diagnosis Using Hybrid Machine Learning Model in Layered Classification Approach." International Journal of Scientific Engineering and Research 12, no. 12 (2024): 12–15. https://doi.org/10.70729/se241223182706.

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Liu, Fangyuan, Bo Qin, and Fengqi Jiang. "Eye Disease Net: an algorithmic model for rapid diagnosis of diseases." PeerJ Computer Science 9 (December 12, 2023): e1672. http://dx.doi.org/10.7717/peerj-cs.1672.

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With the development of science and technology and the improvement of the quality of life, ophthalmic diseases have become one of the major disorders that affect the quality of life of people. In view of this, we propose a new method of ophthalmic disease classification, ED-Net (Eye Disease Classification Net), which is composed of the ED_Resnet model and ED_Xception model, and we compare our ED-Net method with classical classification algorithms, transformer algorithm, more advanced image classification algorithms and ophthalmic disease classification algorithms. We propose the ED_Resnet modu
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Soundharya, Ms R. "Skin Disease Detection Model." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 4146–53. http://dx.doi.org/10.22214/ijraset.2024.62566.

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Abstract: The human skin is a remarkable organ susceptible to a myriad of know and unknown diseases. Many of these ailment are widespread, with some ranking among common worldwide. The complexity of diagnosing these diseases is compounded by challenges such as variations in skin texture, the presence of hair, and diverse skin colors. In some areas have limited access to medical facilities, individuals often neglect early symptoms, leading to exacerbated conditions over time. Furthermore, traditional diagnostic methods for skin diseases are time consuming. To address these challenges, there is
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Shelly Jha, Mohmad Zia, Shardul Mane, Wasiuddin Syed, and Nilesh Bhelkar. "Plant Disease Classification Using Convolutional Neural Networks." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 11 (2024): 2575–80. http://dx.doi.org/10.47392/irjaeh.2024.0354.

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The agricultural sector faces significant losses due to plant diseases, particularly in major crops such as potatoes, tomatoes, and bell peppers. This paper presents a machine learning-based approach to classify diseases in these crops using leaf images. A Convolutional Neural Network (CNN) model was constructed and trained on datasets of healthy leaf images and diseased leaf images from potato, tomato, and bell pepper plants. The model successfully classifies diseases such as Bacterial Spot (for bell peppers), Early Blight, Late Blight, Mosaic Virus, Leaf Mold (for tomatoes), and with a class
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Zhang, Ai-Ping, Guang-xin Wang, Wei Zhang, and Jing-Yu Zhang. "Cardiovascular disease classification based on a multi-classification integrated model." Networks and Heterogeneous Media 18, no. 4 (2023): 1630–56. http://dx.doi.org/10.3934/nhm.2023071.

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<abstract> <p>Cardiovascular disease (CVD) has now become the disease with the highest mortality worldwide and coronary artery disease (CAD) is the most common form of CVD. This paper makes effective use of patients' condition information to identify the risk factors of CVD and predict the disease according to these risk factors in order to guide the treatment and life of patients according to these factors, effectively reduce the probability of disease and ensure that patients can carry out timely treatment. In this paper, a novel method based on a new classifier, named multi-agen
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Yebasse, Milkisa, Birhanu Shimelis, Henok Warku, Jaepil Ko, and Kyung Joo Cheoi. "Coffee Disease Visualization and Classification." Plants 10, no. 6 (2021): 1257. http://dx.doi.org/10.3390/plants10061257.

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Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we presen
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Sadhasivam, Jayakumar, Senthil J, Ganesh R.M, and Chellapan N. "Liver Disease Prediction Using Machine Learning Classification." Webology 18, no. 02 (2021): 441–52. http://dx.doi.org/10.14704/web/v18si02/web18293.

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People have disorder of liver that require medical care at correct time. It is utmost important to find the disease before it elapse the curable stage. Significantly, much of understanding of organ development has arisen from analyses of patients with liver deficiencies. Data mining is beneficial to find the disease at early stage based on the factors that can be gathered by performing test on the patient. Nowadays, around 65 % of the population in India are eating junk foods which minimize the metabolism rate and effect liver in many ways. In recent years, liver disorders have excessively inc
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Dissertations / Theses on the topic "Disease classification model"

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Udaya, Kumar Magesh Kumar. "Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset." Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-5596.

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Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on
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Monestime, Judith. "ICD-10-CM Implementation Strategies: An Application of the Technology Acceptance Model." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/1909.

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The United States is one of the last countries to transition to the 10th edition of the International Classification of Diseases (ICD-10) coding system. The move from the 35-year-old system, ICD-9, to ICD-10, represents a milestone in the transformation of the 21st century healthcare industry. All covered healthcare entities were mandated to use the ICD-10 system on October 1, 2015, to justify medical necessity, an essential component in determining whether a service is payable or not. Despite the promising outcomes of this shift, more than 70% of healthcare organizations identified concerns r
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Bacher, Michael, Richard Dodel, Bayan Aljabari та ін. "CNI-1493 inhibits Aβ production, plaque formation, and cognitive deterioration in an animal model of Alzheimer's disease". Rockefeller University Press, 2008. https://tud.qucosa.de/id/qucosa%3A26296.

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Alzheimer's disease (AD) is characterized by neuronal atrophy caused by soluble amyloid β protein (Aβ) peptide "oligomers" and a microglial-mediated inflammatory response elicited by extensive amyloid deposition in the brain. We show that CNI-1493, a tetravalent guanylhydrazone with established antiinflammatory properties, interferes with Aβ assembly and protects neuronal cells from the toxic effect of soluble Aβ oligomers. Administration of CNI-1493 to TgCRND8 mice overexpressing human amyloid precursor protein (APP) for a treatment period of 8 wk significantly reduced Aβ deposition. CNI-1493
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RESAZ, ROBERTA. "Hepatocellular adenoma classification using MRI and identification of biomarkers for prediction of liver tumor onset in a mouse model of glycogen storage disease type1a." Doctoral thesis, Università degli studi di Genova, 2019. http://hdl.handle.net/11567/945553.

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The purpose of my thesis is to specify the feminine world (compared to the masculine one), from the viewpoint of onomastics, in the reality of Chieri -Turin-), a settlement with a thousand-year history, in the transition from the end of the Middle Ages to the early Modern Age. My investigation is based on the surveys carried out on fraternities’ registers and baptismal registers from the period between 1508 and 1600. The wish is to underline also social and linguistic transformations as they are discernible through the process of individual naming. For data processing, a digital archive was cr
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Lin, Shu-Chuan. "Robust estimation for spatial models and the skill test for disease diagnosis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26681.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Lu, Jye-Chyi; Committee Co-Chair: Kvam, Paul; Committee Member: Mei, Yajun; Committee Member: Serban, Nicoleta; Committee Member: Vidakovic, Brani. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Saghafi, Abolfazl. "Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model". Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6946.

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The reach of technological innovation continues to grow, changing all industries as it evolves. In healthcare, technology is increasingly playing a role in almost all processes, from patient registration to data monitoring, from lab tests to self-care tools. The increase in the amount and diversity of generated clinical data requires development of new technologies and procedures capable of integrating and analyzing the BIG generated information as well as providing support in their interpretation. To that extent, this dissertation focuses on the analysis and processing of biomedical signals,
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Hu, Wenbiao. "Applications of Spatio-temporal Analytical Methods in Surveillance of Ross River Virus Disease." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16109/1/Wenbiao_Hu_Thesis.pdf.

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The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic
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Hu, Wenbiao. "Applications of Spatio-temporal Analytical Methods in Surveillance of Ross River Virus Disease." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16109/.

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The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic
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Kaszta, Zaneta. "Using remotely-sensed habitat data to model space use and disease transmission risk between wild and domestic herbivores in the African savanna." Doctoral thesis, Universite Libre de Bruxelles, 2017. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/253820.

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The interface between protected and communal lands presents certain challenges for wildlife conservation and the sustainability of local livelihoods. This is a particular case in South Africa, where foot-and-mouth disease (FMD), mainly carried by African buffalo (Syncerus caffer) is transmitted to cattle despite a fence surrounding the protected areas.The ultimate objective of this thesis was to improve knowledge of FMD transmission risk by analyzing behavioral patterns of African buffalo and cattle near the Kruger National Park, and by modelling at fine spatial scale the seasonal risk of cont
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Wu, Tsung-Lin. "Classification models for disease diagnosis and outcome analysis." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44918.

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In this dissertation we study the feature selection and classification problems and apply our methods to real-world medical and biological data sets for disease diagnosis. Classification is an important problem in disease diagnosis to distinguish patients from normal population. DAMIP (discriminant analysis -- mixed integer program) was shown to be a good classification model, which can directly handle multigroup problems, enforce misclassification limits, and provide reserved judgement region. However, DAMIP is NP-hard and presents computational challenges. Feature selection is important in
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Books on the topic "Disease classification model"

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U, Mohr, and International Agency for Research on Cancer., eds. International classification of rodent tumours. International Agency for Research on Cancer, 1992.

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U, Mohr, ed. The mouse. Springer, 2001.

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Belokonev, Vladimir, Sergey Pushkin, Valeriy Zaharov, et al. Treatment of patients with ventral hernia and obesity. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1873828.

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The monograph is devoted to the pathogenesis, clinic and treatment of patients with ventral hernia and obesity up to grade II. The features of the development of the disease, accompanied by the development of a cutaneous-subcutaneous apron — panniculus, which significantly affects the dynamics of hernia enlargement and the quality of life of the patient, are described. The classification of the panniculus, developed on the basis of the proposed mathematical model of the development of the pathological process in the abdominal wall, and the justification of indications for its removal are prese
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1963-, Feng Zhi, and Long Ming, eds. Viral genomes: Diversity, properties, and parameters. Nova Science Publishers, 2009.

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D, Nadler Ronald, ed. Medical primatology: History, biological foundations and applications. Taylor & Francis, 2002.

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Upadhyay, Ashish, Lesley A. Inker, and Andrew S. Levey. Chronic kidney disease. Edited by David J. Goldsmith. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0094.

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The conceptual model, definition, and classification of chronic kidney disease (CKD) were first described in the National Kidney Foundation’s Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines in 2002 and have had a major impact on patient care and research. Since this publication there has been an increased recognition that the cause of CKD influences progression and complications. In addition, epidemiologic reports from diverse populations have consistently shown graded relations between higher albuminuria and adverse kidney outcomes and complications, in addition to, and independ
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Huda, Ahmed Samei. The Medical Model in Mental Health. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198807254.001.0001.

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The medical model is a biopsychosocial model assessing a patient’s problems and matching them to the diagnostic construct using pattern recognition of clinical features. Diagnostic constructs allow for researching, communicating, teaching, and learning useful clinical information to influence clinical decision-making. They also have social and administrative functions such as access to benefits. They may also help explain why problems occur. Diagnostic constructs are used to describe diseases/syndromes and also other types of conditions such as spectrums of conditions. Treatments in medicine a
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Hans, Steiner, Daniels Whitney, Kelly Michael, and Stadler Christina. Taxonomy, Classification, and Diagnosis of Disruptive Behavior Disorders. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190265458.003.0002.

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This chapter traces the development of diagnoses attempting to capture antisocial and aggressive behavior. The chapter provides a careful discussion of the advantages of the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases systems and their diagnostic grouping. Tracing the processes by which these diagnoses were created, the hidden and obvious problems in the current taxonomy are laid bare. The model of developmental psychopathology, of which disruptive behavior disorders arguably have been called a model disorder, provides concluding comments,
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Plutynski, Anya. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199967452.003.0001.

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In this introduction, I discuss how biological explanations of disease are like and unlike the explanations offered by engineers or car mechanics. I also discuss how the study of cancer raises a variety of philosophical puzzles: concerning natural classification, the demarcation of disease and health, disease versus disease risk, evidential inference and causation, the scope and limits of theoretical models in science, and the nature of scientific explanation.
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Kyle, Simon D., and Colin A. Espie. Insomnias. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0019.

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This chapter reviews contemporary understanding of insomnia disorder. Specifically, it examines insomnia prevalence and associated morbidity, clinical approaches to the evaluation of insomnia, and models of insomnia development and maintenance. Recent evolution of insomnia classification is emphasized, together with novel perspectives on the role of insomnia in conferring risk for mental and physical disease. It is concluded that the development of theoretical models that integrate both psychological and neurobiological levels of analysis will provide a fuller understanding of insomnia pathoph
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Book chapters on the topic "Disease classification model"

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Chauhan, Ritu, Rajesh Jangade, and Ruchita Rekapally. "Classification Model for Prediction of Heart Disease." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5699-4_67.

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Kulkarni, Janhavi, Poorvi Verma, and Snehal V. Laddha. "Monkeypox Disease Classification Using HOG-SVM Model." In Data Science and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7862-5_13.

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Das, Himansu, Bighnaraj Naik, and H. S. Behera. "Disease Classification Using Linguistic Neuro-Fuzzy Model." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_5.

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Bilancia, Massimo, and Alessio Pollice. "A Spatial Clustering Hierarchical Model for Disease Mapping." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17111-6_17.

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Kumar, Raj, Tushar Agrawal, Vinayak Dhar Dwivedi, and Harsh Khatter. "Potato Leaf Disease Classification Using Deep Learning Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62217-5_16.

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Kadaru, Bala Brahmeswara, M. Uma Maheswara Rao, and S. Narayana. "FTD Tree Based Classification Model for Alzheimer’s Disease Prediction." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1498-8_60.

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Shinde, Nirmala, and Asha Ambhaikar. "Fine-Tuned Xception Model for Potato Leaf Disease Classification." In Proceedings of Fifth International Conference on Computing, Communications, and Cyber-Security. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2550-2_47.

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Abdulsalam, Sulaiman Olaniyi, Micheal Olaolu Arowolo, and Oroghi Ruth. "Stroke Disease Prediction Model Using ANOVA with Classification Algorithms." In Artificial Intelligence in Medical Virology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0369-6_8.

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Gaur, Pramod, Vatsal Malaviya, Abhay Gupta, et al. "An Optimal Model Selection for COVID 19 Disease Classification." In EAI/Springer Innovations in Communication and Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15816-2_20.

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Jeba Priya, S., S. Joshua Jaistein, G. Naveen Sundar, and T. Raja Sundrapandiyanleebanon. "Deep Learning-Based Enhanced Classification Model for Pneumonia Disease." In Smart Computing Techniques and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1502-3_29.

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Conference papers on the topic "Disease classification model"

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Umer, Muhammad, Ali Javed, and Hussain Dawood. "EnhancedNet Deep Learning Model for Plant Disease Classification." In 2024 1st International Conference on Logistics (ICL). IEEE, 2024. https://doi.org/10.1109/icl62932.2024.10788598.

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Jayalakshmi, V. Jalaja, V. Geetha, and R. K. Kavitha. "A Hybrid Model for Parkinson's Disease Detection Classification." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689845.

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Das, Goutami, Ritunsa Mishra, Saumendra Kumar Mohapatra, and Mihir Narayan Mohanty. "Optimized Machine Learning Model for Alzheimer’s Disease Classification." In 2024 International Conference on Signal Processing and Advance Research in Computing (SPARC). IEEE, 2024. https://doi.org/10.1109/sparc61891.2024.10829252.

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Saini, Archana, Kalpna Guleria, and Shagun Sharma. "Mango Leaf Disease Classification Utilizing InceptionV3 Deep Learning Model." In 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI). IEEE, 2024. https://doi.org/10.1109/cvmi61877.2024.10782246.

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Sharma, Jatin, Deepak Kumar, and Abhiraj Malhotra. "Weed Disease Classification using CNN and Inception V3 Model." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10837925.

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Huang, Jun, Zhiwei Wang, Ming Xu, Longhua Ma, Wenxiang Wu, and Jiaxiang Cao. "Enhancing Few-Shot Plant Disease Classification with Diffusion Model." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864650.

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Saini, Archana. "EfficientNetB3-Based Model for High-Accuracy Retinal Disease Classification." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947812.

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Thavasimuthu, Anitha, P. Jesu Jayarain, and Narmatha C. "Classification of Alzheimer's Disease using Attention UNet-MobileNet Model." In 2025 4th International Conference on Computing and Information Technology (ICCIT). IEEE, 2025. https://doi.org/10.1109/iccit63348.2025.10989374.

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Saini, Archana. "Efficient Banana Leaf Disease Classification Using Vision Transformer (ViT) Model." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841002.

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Sitaraman, Surendar Rama, Myasar Mundher Adnan, K. Maharajan, R. Krishna Prakash, and R. Dhilipkumar. "A Classification of Inflammatory Bowel Disease using Ensemble Learning Model." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796250.

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