Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods
PaperId: :  21711
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 5 2023
DUI:    16.0415/IJARIIE-21711
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Prakash R VS.E.A. College of Engineering & Technology
Dr. K Sundeep KumarS.E.A. College of Engineering & Technology

Abstract

Computer Science
Diabetic Foot Ulcer, LBP, LIPC
The identification of Diabetic Foot ulcers is critical for patients' early diagnosis and therapy planning. Image processing algorithms have developed as useful tools for automatic and reliable ulcer diagnosis from medical imaging data in recent years. This research describes a novel approach for detecting Diabetic Foot ulcers using image processing techniques. A series of preprocessing processes are used in the proposed method to improve the quality of Diabetic Foot pictures and reduce noise. Image scaling, noise reduction, and contrast improvement are all included. Following preprocessing, image segmentation algorithms are used to isolate probable ulcer locations and separate the Diabetic Foot region from the backdrop. Various feature extraction approaches are used to extract significant features from the segmented Diabetic Foot regions for ulcer identification. These criteria capture crucial ulcer properties such as form, texture, and intensity fluctuations. The retrieved features are then used to train a classifier to distinguish between ulcer and non-ulcer regions. Experiments are carried out using a Kaggle dataset of Diabetic Foot pictures encompassing both ulcer and non-ulcer instances to assess the efficacy of the suggested technique. The findings show that the proposed method for detecting Diabetic Foot ulcers is highly accurate and efficient. Comparisons with existing approaches demonstrate the suggested method's advantages in terms of detection accuracy and computing efficiency. Overall, the suggested image-based Diabetic Foot ulcer detection system has significant promise for supporting medical professionals in the early detection of Diabetic Foot ulcers. It has the potential to improve patient outcomes by allowing for timely intervention and personalized treatment regimens.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Prakash R V, and Dr. K Sundeep Kumar. "Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 5 2023 Page 1173-1182
MLA Prakash R V, and Dr. K Sundeep Kumar. "Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods." International Journal Of Advance Research And Innovative Ideas In Education 9.5(2023) : 1173-1182.
APA Prakash R V, & Dr. K Sundeep Kumar. (2023). Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods. International Journal Of Advance Research And Innovative Ideas In Education, 9(5), 1173-1182.
Chicago Prakash R V, and Dr. K Sundeep Kumar. "Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 5 (2023) : 1173-1182.
Oxford Prakash R V, and Dr. K Sundeep Kumar. 'Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, 2023, p. 1173-1182. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Detection_of_Diabetic_Foot_Ulcer_from_Kaggle_datasets_using_LBP_and_LIPC_Methods_ijariie21711.pdf (Accessed : ).
Harvard Prakash R V, and Dr. K Sundeep Kumar. (2023) 'Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods', International Journal Of Advance Research And Innovative Ideas In Education, 9(5), pp. 1173-1182IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Detection_of_Diabetic_Foot_Ulcer_from_Kaggle_datasets_using_LBP_and_LIPC_Methods_ijariie21711.pdf (Accessed : )
IEEE Prakash R V, and Dr. K Sundeep Kumar, "Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, pp. 1173-1182, Sep-Oct 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Detection_of_Diabetic_Foot_Ulcer_from_Kaggle_datasets_using_LBP_and_LIPC_Methods_ijariie21711.pdf [Accessed : ].
Turabian Prakash R V, and Dr. K Sundeep Kumar. "Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 5 ().
Vancouver Prakash R V, and Dr. K Sundeep Kumar. Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(5) : 1173-1182. Available from: https://ijariie.com/AdminUploadPdf/Detection_of_Diabetic_Foot_Ulcer_from_Kaggle_datasets_using_LBP_and_LIPC_Methods_ijariie21711.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Leveraging AWS for Developing and Hosting a Dynamic Food Ordering Web ApplicationComputer EngineeringAfshin Khanam Download
Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud StorageInformation TechnologyKarthik Kushala Download
E-Commerce Fraud Detection Based on Machine Learning TechniquesComputer Science EngneeringVikram Ankush Ade Download
Detection of Phishing Website Using Gradient Boosting AlgorithmComputer Science and EngineeringYAWALKAR PRASAD PRAMOD Download
Property Dealing WebComputer Science EngineeringYash Chaudhari Download
Reinforcement Learning for the Evolution of Antimicrobial Nano formulationsmachine learningMadhusudan Download
SecuraVault: A secured blockchain based cloud storage systemComputer Engineering Anshika Jaiswal Download
Autoimmune Disease Detection in women Using Machine Learning Approachcomputer science EngineeringJ. L. V. S. Download
Medicine Overdose Detection System Using Machine LearningComputer Science EngineeringDr.Somashekhar B M Download
Home Price Prediction Using Machine LearningComputer Science & Engineering G Tushar Download
Heat diseases prediction using machine learningComputer EngineeringProf. Meghashree M B Download
GRIDSHIELD AIComputer EngineeringDr. Archana B Download
Diabetic Retinopathy Detection with AI InsightsComputer EngineeringJay Mahesh Gurav Download
Personalized Fitness Segmentation with Actionable InsightsMachine LearningAnju Tiwari Download
Sentiment-Based Machine Learning Approach for Mapping Citizen ProblemsComputer science and EngineeringDr. Madhu B K Download
12