TY  - EJOU
AU  - Rawashdeh, Majdi 
AU  - Obaidat, Muath A. 
AU  - Abouali, Meryem 
AU  - Salhi, Dhia Eddine 
AU  - Thakur, Kutub 

TI  - An Effective Lung Cancer Diagnosis Model Using Pre-Trained CNNs
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 1
SN  - 1526-1506

AB  - Cancer is a formidable and multifaceted disease driven by genetic aberrations and metabolic disruptions. Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer, which is also the top cause of death worldwide. The malignancy has a terrible 5-year survival rate of 19%. Early diagnosis is critical for improving treatment outcomes and survival rates. The study aims to create a computer-aided diagnosis (CAD) that accurately diagnoses lung disease by classifying histopathological images. It uses a publicly accessible dataset that includes 15,000 images of benign, malignant, and squamous cell carcinomas in the lung. In addition, this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network (CNN) based on pre-trained models such as AlexNet, VGG (Visual Geometry Group)16, ResNet-50, and VGG19, after hyper-tuning these models by optimizing factors such as batch size, learning rate, and epochs. The proposed (CNN + VGG19) model achieves the highest accuracy of 99.04%. This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological images. This study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification, giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.
KW  - Lung cancer; machine learning; computer aided diagnosis; CNN; medical imaging; transfer learning

DO  - 10.32604/cmes.2025.063765