Lung cancer diagnosis with GAN supported deep learning models.

Abstract

Background: Lung cancer is a leading cause of cancer-related deaths worldwide, making early diagnosis crucial for improving treatment success and survival rates. Traditional diagnostic methods, such as biopsy and manual CT image interpretation, are time-consuming and prone to variability, highlighting the need for more efficient and accurate tools. Advances in deep learning offer promising solutions by enabling faster and more objective medical image analysis.

Objective: This study aims to classify benign, malignant, and normal lung CT images using advanced deep learning techniques, including a specially developed CNN model, to improve diagnostic accuracy.

Methods: A dataset of 1097 lung CT images was balanced using GANs and preprocessed with techniques like histogram equalization and noise reduction. The data was split into 70% training and 30% testing sets. Models including VGG19, AlexNet, InceptionV3, ResNet50, and a custom-designed CNN were trained. Additionally, Faster R-CNN-based region proposal methods were integrated to enhance detection performance.

Results: The custom CNN model achieved the highest accuracy at 99%, surpassing other architectures like VGG19, which reached 97%. The Faster R-CNN integration further improved sensitivity and classification precision.

Conclusion: The results demonstrate the effectiveness of GAN-supported deep learning models for lung cancer classification, highlighting their potential clinical applications for early detection and diagnosis.