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Oral Session B-3 : Biomedical Applications

AI-driven Deep Learning Analysis of Leishmania Parasites in Microscopic Images

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    ICNGC 2025 The 11th International Conference on Next Generation Computing 2025 (2025.12)바로가기
  • 페이지
    pp.223-226
  • 저자
    Abdul Rafay, Muhammad Sajid Farooq, Umer Farooq, Muhammad Kamran, Salman Muneer, Muhammad Tayyab Khan
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A478499

원문정보

초록

영어
This Leishmaniasis is common skin lesion parasitic disease caused by Leishmania protozoan parasites on exposed body and its polymorphic nature complicates to diagnosis because the lesion may create confusion with other dermatoses likewise fungi, bacteria and non-infectious diseases. The molecular techniques, microscopy, culture, and rapid diagnostic test are conventional methods that are timeconsuming, expensive, susceptible to errors with limited resources in health care services. Early diagnosis with timely identification of multifaceted Leishmaniasis is aided to selection of therapy and provide comfort to patient to combating with it. The promising integration of artificial intelligence (AI) with medical diagnostics has efficacy in numerous fields of identification of diseases as in Dermatology research. The fast, efficient and automatic diagnosing of leishmaniasis with microscopic images of lesion's seamer with VGG-16 deep learning (DL) model is the approach to reach the objective of this designed research to identify the negative and positive results. The exceptional performance of designed VGG-16 is achieved with accuracy of 88.14%, precision 100%, sensitivity 77.42%, specificity 100%, F1-score 0.87%, and ROC curve 97%. The proposed modified VGG-16 model is more precise, swift, reliable, efficient, effectual, economical and user-friendly substitute to address all key factors than human resource to find the leishmaniasis affected that may support medical care services.

목차

Abstract
I. INTRODUCTION
II. LITRATURE REVIEW
III. MATERIALS AND METHODS
A. DATASET
IV. SIMULATION AND RESULTS
A. RESULTS
V. CONCLUSION AND FUTURE WORK
REFERENCES

키워드

VGG-16 DL algorithm model Leishmaniasis Artificial Intelligence Skin disease Dermatology

저자

  • Abdul Rafay [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
  • Muhammad Sajid Farooq [ Department of Cyber Security NASTP Institute of Information Technology, Lahore 58810, Pakistan ]
  • Umer Farooq [ Department of Computing Hamdard University Karachi Sindh, Pakistan ]
  • Muhammad Kamran [ Department of Computer Science Minhaj University Lahore, Pakistan ]
  • Salman Muneer [ Department of Computer Science University of Central Punjab (UCP) , Lahore, Pakistan. ]
  • Muhammad Tayyab Khan [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

이 권호 내 다른 논문 / 한국차세대컴퓨팅학회 학술대회 ICNGC 2025 The 11th International Conference on Next Generation Computing 2025

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