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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Machine Learning Algorithm Using Diabetic Detection Based on Tongue Images |
| Authors Name: | N. Kavithamani , M.Kasiselvanathan , S.Prabhavathy |
| Download E-Certificate: | Download |
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IJRTI_205044
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| Published Paper Id: | IJRTI2506219 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | |
| Abstract: | In today's the world at large, the tongue key role in human communication and overall health. It is intricately connected to various parts of the body, making it an important indicator of overall well-being. One fascinating application of tongue analysis is in the field of diabetic classification. This project focuses on detecting diabetes using tongue images through the extraction of texture attribute and employing Convolutional Neural Network (CNN) attribute. The process begins by inputting tongue images, which are then subjected to global feature extraction to capture relevant characteristics. Subsequently, CNN classifiers are utilized for the classification task. This method showcases the potential of using non-invasive and easily accessible imaging techniques for medical diagnosis and monitoring. In day to day an artificial intelligence and machine learning methods is more used with the diagnosis of diseases. In this paper, the reference data is used by Diabetes Diagnosis Data to evaluate the proposed method. This method used with the help from the auto-encoder neural network is of a deep network built made in two ways. In the first method, Auto encoders are a type of neural network used to learn efficient codings of unlabeled data. autoencoders might be used to preprocess and reduce the dimensionality of the data related to the disease diagnosis, extracting relevant features that are critical for accurate diagnosis. In the second method, evaluated with the help of the final learning machine and the deep network has been constructed. This machine might take the outputs from the deep network as inputs and apply additional learning algorithms (like support vector machines or decision trees) to enhance prediction accuracy. |
| Keywords: | Diabetes detection, CNN, Auto-encoder neural network, back propagation delay, final learning machine, Support vector machine. |
| Cite Article: | "Machine Learning Algorithm Using Diabetic Detection Based on Tongue Images", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.c144-c153, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506219.pdf |
| Downloads: | 000383 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2506219
Registration ID:205044
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier):
Page No: c144-c153 Country: Coimbatore, Tamil Nadu, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506219 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506219 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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