Earticle

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

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

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