An Intelligent Machine Learning Framework for Early Lung Cancer Diagnosis

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to delayed diagnosis and limited treatment options available at advanced stages of the disease. Early detection plays a crucial role in improving survival rates and enhancing patient outcomes. To address this challenge, this study proposes an advanced machine learning-based framework for the early diagnosis of lung cancer using clinical and medical imaging data. The proposed approach analyzes patient demographics, medical history, radiological scans, and relevant biomarkers to identify subtle indicators of early-stage lung cancer. The methodology incorporates data preprocessing, feature extraction, and feature engineering techniques to improve the quality and relevance of the input data. Computed Tomography (CT) images are utilized due to their effectiveness in providing detailed information about tumor size, shape, location, and surrounding tissue characteristics. Following preprocessing, image segmentation is performed to isolate regions of interest, and significant features are extracted for further analysis. A Convolutional Neural Network (CNN) classifier is employed to learn complex patterns from the extracted features and classify lung nodules as benign or malignant. Advanced model interpretation techniques are also integrated to enhance transparency and identify key factors influencing the model’s predictions. Experimental evaluation on large retrospective datasets demonstrates that the proposed framework achieves superior performance compared to existing state-of-the-art methods. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of the model. The results indicate that the proposed machine learning framework can significantly improve early lung cancer detection, supporting healthcare professionals in making informed clinical decisions and enabling timely intervention that may ultimately save lives..