LUNG CANCER DETECTION AND CLASSIFICATION USING DEEP LEARNING: A VGG-16 BASED APPROACH

Abstract

Lung cancer is still one of the most common causes of cancer-related fatalities around the world. This is mostly because it is hard to identify early and the symptoms are hard to understand. Early detection and precise classification of lung cancer can greatly enhance patient outcomes by facilitating prompt and tailored therapy approaches.With an emphasis on computed tomography (CT) scans, this thesis provides a thorough education on the application of deep learning algorithms for the automated categorization and detection of lung cancer from medical imaging data.

The proposed methodology involves the development and training of CNN architectures tailored for image-based analysis. A carefully curated dataset of annotated lung CT images was employed to ensure model robustness and generalizability. To improve image quality and extract important characteristics, preprocessing methods such segmentation, noise reduction, and normalization were used. Various CNN models were implemented and evaluated, including transfer learning approaches using pre-trained networks to enhance performance in scenarios with limited medical data.

Efficiency was assessed using standard metrics like accuracy, recall, precision, F1-score, and area under the receiver's operating characteristic curve (AUC-ROC).  The results show that the optimized deep learning model achieves a high degree of accuracy in both recognizing the stages of cancer progression and differentiating between benign and malignant nodules.  The results confirm deep learning's potential as a trustworthy tool for helping radiologists make clinical decisions.

This study emphasizes the significance of incorporating advanced computational methods with medical imaging for improving diagnostic accuracy and decreasing the load on healthcare systems. The study also highlights challenges such as dataset imbalance, model interpretability, and the need for cross-institutional validation, offering recommendations for future work in this critical area of medical diagnostics.