Call for Papers:Vol.12 Issue.2

Submission
Last date		 28-Apr-2026
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-12,Issue-2. 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: :  Implementation Diabetic Foot Ulcer Types Detection using Neural Networks
PaperId: :  21712
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-21712
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, Bangalore
Dr. K Sundeep KumarS.E.A. College of Engineering & Technology, Bangalore

Abstract

Computer Science
Diabetic foot Ulcer, K-means clustering and Neural Networks, RGB, HIS, etc
Diabetic Foot Ulcer Infection is one of the essential causes that reduces quantity and degrades quality of the Medical Diagnosis merchandises. Diabetic Foot Ulcer Infections have turned into a terrible as it can cause significant reduction in both quality and quantity of Medical Diagnosis products. Images form important data and information in biological sciences. Until recently photography was the only method to reproduce and report such data. It is difficult to quantify or treat the photographic data mathematically. This project, classifies the Diabetic Foot Ulcer at hand into infected and non-infected classes. The developing software provides a fast and accurate method in which the Diabetic foot Ulcer Infections are detected and classified using k-means based segmentation and neural networks-based classification. Most common Infections seen in the foot of Tapioca and Mango are discussed here for this approach. In this paper, respectively, the applications of K-means clustering and Neural Networks (NNs) have been formulated for clustering and classification of Infections that effect on Diabetic Foot Ulcer foot. Recognizing the Infection is mainly the purpose of the proposed approach. Thus, the proposed Algorithm was tested on three types of diabetic foot ulcer described namely neuropathic, neuroischaemic, and ischaemic. The experimental results indicate that the proposed approach is a valuable approach, which can significantly support an accurate detection of Diabetic foot Ulcer Infections in a little computational effort. This project gives 95% of efficiency using MATLAB simulation results.

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. "Implementation Diabetic Foot Ulcer Types Detection using Neural Networks" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 5 2023 Page 1165-1172
MLA Prakash R V, and Dr. K Sundeep Kumar. "Implementation Diabetic Foot Ulcer Types Detection using Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education 9.5(2023) : 1165-1172.
APA Prakash R V, & Dr. K Sundeep Kumar. (2023). Implementation Diabetic Foot Ulcer Types Detection using Neural Networks. International Journal Of Advance Research And Innovative Ideas In Education, 9(5), 1165-1172.
Chicago Prakash R V, and Dr. K Sundeep Kumar. "Implementation Diabetic Foot Ulcer Types Detection using Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 5 (2023) : 1165-1172.
Oxford Prakash R V, and Dr. K Sundeep Kumar. 'Implementation Diabetic Foot Ulcer Types Detection using Neural Networks', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, 2023, p. 1165-1172. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Implementation_Diabetic_Foot_Ulcer_Types_Detection_using_Neural_Networks_ijariie21712.pdf (Accessed : ).
Harvard Prakash R V, and Dr. K Sundeep Kumar. (2023) 'Implementation Diabetic Foot Ulcer Types Detection using Neural Networks', International Journal Of Advance Research And Innovative Ideas In Education, 9(5), pp. 1165-1172IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Implementation_Diabetic_Foot_Ulcer_Types_Detection_using_Neural_Networks_ijariie21712.pdf (Accessed : )
IEEE Prakash R V, and Dr. K Sundeep Kumar, "Implementation Diabetic Foot Ulcer Types Detection using Neural Networks," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, pp. 1165-1172, Sep-Oct 2023. [Online]. Available: http://ijariie.com/AdminUploadPdf/Implementation_Diabetic_Foot_Ulcer_Types_Detection_using_Neural_Networks_ijariie21712.pdf [Accessed : ].
Turabian Prakash R V, and Dr. K Sundeep Kumar. "Implementation Diabetic Foot Ulcer Types Detection using Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 5 ().
Vancouver Prakash R V, and Dr. K Sundeep Kumar. Implementation Diabetic Foot Ulcer Types Detection using Neural Networks. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(5) : 1165-1172. Available from: http://ijariie.com/AdminUploadPdf/Implementation_Diabetic_Foot_Ulcer_Types_Detection_using_Neural_Networks_ijariie21712.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Unsupervised Contribution-Oriented Learning Model for Social Influence DetectionComputer EngineeringSnehal Mahjaan Download
DESIGN AND IMPLEMENTATION OF A BLUETOOTH-CONTROLLED ROBOTIC CAR USING ARDUINOComputer Mr. Swapnil Sanjay Bafana Download
AI-DRIVEN DEEPFAKE IDENTIFICATION IN REAL TIMEComputer EngineeringPavan Gajanan Bhonde Download
RESQ-BOTComputer Engineering Tiparkar Prathamesh Navnath Download
A Critical Review and Modern Contextualization of the 2009 Distributed Real-Time Computer Network Architecture (DRNA)Computer EngineeringNandishwar EN Download
Block-Chain Based Document Verification System using IPFSComputer EngineeringAkash Santosh Devade Download
A COMPREHENSIVE REVIEW OF DUAL FEATURE-BASED INTRUSION DETECTION SYSTEM FOR IoT NETWORK SECURITYComputer Science and EngineeringShrinidhi Hegde Download
Civica AI: A Politician-Centric Grievance Redressal and Service DirectoryComputer EngineeringKaranjule Dhanashri Bhausaheb Download
A Deep Learning Framework for Mood-Based Music Recommendation via Facial Expression AnalysisComputer Vaibhav Ashok Bhangare Download
GREEN NETWORKING: ENERGY-EFFICIENT PROTOCOLS AND SUSTAINABLE NETWORK DESIGN: A COMPREHENSIVE REVIEWComputer Science and EngineeringPradeep Nayak Download
DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNINGComputer EngineeringSiddharth Shukracharya Rokade Download
PERSONALITY PREDICTION USING MLComputer EngineeringTanvi Dashrath Bhagat Download
Crop Disease Detectioncomputer Mansi Sunil Sansare Download
NEXT-GEN PLANT DISEASE DIAGNOSIS WITH GEN-AI AND DEEP LEARNINGComputer ScienceGuruprasad K Download
GEN-AI POWERED PIGEON PEA LEAF-DISEASE DETECTION USING DEEP-LEARNING AND COMPUTER-VISIONComputer ScienceRajshekar G Download
12