Earticle

현재 위치 Home

Oral Session B-3 : Biomedical Applications

Predictive Analysis of Lung Cancer empowered with Transfer Learning and Explainable AI

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.231-233
  • 저자
    Arooj Fatima, Shahid Iqbal, Abdul Hannan Khan, Roshaan Fatima, Reyaz Ahmad, Muhammad Adnan Khan
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A478501

원문정보

초록

영어
The need to detect lung cancer accurately and early has been brought out through the fact that lung cancer has remained one of the top causes of cancer-related deaths in the world. A CT scan is usually interpreted manually which makes this time consuming and subjective. This paper hypothesizes that an explainable implementation of automated deep learning can be used to classify lung cancer based on the transfer learning models (VGG16, VGG19, ResNet50, and EfficientNetB3) on the IQ-OTH/NCCD dataset. The dataset was stratified by means of the SMOTE and was split into 80 and 20 percent training and validation subsets, respectively. All the pretrained CNNs were fine-tuned with the Adam optimizer and categorical cross-entropy loss. VGG16 performed the best and had a validation accuracy of 98.64, precision and recall of 98, and ROCAUC of 99. Visualization of tumor regions was done using explainable AI techniques (Grad-CAM, LIME), which are interpretable and have diagnostic transparency. The suggested framework proves that transfer learning combined with XAI is more effective in terms of accuracy and reliability in diagnosing medical images and is one of the steps to clinically reliable smart healthcare systems.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. METHODOLGY
A. Dataset Gathering
B. Data Preparation
C. Deep Learning Models
D. Model Training
E. Explainable AI (XAI)
F. Performance Evaluation
IV. RESULTS
A. Result of EfficientNetB3 Model
B. Result of ResNet50 Model
C. Result of VGG19 Model
D. Result of VGG16 Model

키워드

Deep Learning Lung Cancer Medical Images VGG 16 VGG 19 ResNet50 EfficientNetB3 Explainable AI Grad CAM LIME.

저자

  • Arooj Fatima [ Department of Computer Science, Green International University Lahore, Pakistan ]
  • Shahid Iqbal [ Department of Computer Science, Green International University Lahore, Pakistan ]
  • Abdul Hannan Khan [ Department of Computer Science, Green International University Lahore, Pakistan ]
  • Roshaan Fatima [ School of Computing, Horizon University College, Ajman, UAE ]
  • Reyaz Ahmad [ School of General Education, Horizon University College, Ajman, UAE ]
  • Muhammad Adnan Khan [ School of Computing, Horizon University College, Ajman, UAE ] Corresponding Author

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

    피인용수 : 0건 (자료제공 : 네이버학술정보)

    함께 이용한 논문 이 논문을 다운로드한 분들이 이용한 다른 논문입니다.

      출처 : 네이버학술정보

        0개의 논문이 장바구니에 담겼습니다.

        페이지 저장
        소속기관 조회
        이용자님의 소속기관(단체)이 서비스에 가입되어 있는지 확인해 보십시오.
        기관회원에 소속되어 있는 이용자는 원문을 무료로 이용할 수 있습니다.