DEEPLUNGSCAN: EARLY DETECTION OF LUNG CANCER USING ENHANCED CNN-BASED CT IMAGE ANALYSIS

Medical imaging tools play a critical role in the early detection and treatment monitoring of lung cancer.Various imaging modalities, including chest X-ray, magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and molecular imaging techniques, have been widely utilized for lung cancer diagnosis.However, these techniques have limitations, such as the inability to automatically classify cancerous images, making them less suitable for patients with co-existing pathologies.Therefore, there is an urgent need for a highly sensitive and accurate method for early lung cancer diagnosis.Deep learning has emerged as a rapidly advancing field in medical imaging, with promising applications across image-based and texture-based data analysis.Utilizing deep learning-based tools enables clinicians to detect and classify lung nodules more efficiently and with greater accuracy.This study proposes an advanced Convolutional Neural Network (CNN) model for detecting lung cancer from chest CT scan images.The proposed model classifies CT images into four categories: adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal tissue.It demonstrates superior accuracy compared to a traditional machine learning approach, the Naive Bayes Classifier (NBC).Additionally, the evaluation metrics confirm the effectiveness of the proposed deep CNN model in supporting clinical experts for improved diagnostic outcomes.