CLASSIFICATION AND PREDICTION OF LUNG CANCER USING DEEP LEARNING TECHNIQUES

COMPUTER SCIENCE AND ENGINEERING

Lung cancer remains a significant global health challenge, with early detection pivotal in improving patient outcomes. This project aims to harness the power of deep learning techniques to develop a robust and efficient system for the classification and prediction of lung cancer, thereby enhancing diagnostic accuracy and patient care. The project begins with the collection of a comprehensive dataset of medical images, including lung CT scans, encompassing both cancerous and non-cancerous cases, and deep learning architecture designed to classify patient CT scan reports into three risk categories: low risk, moderate risk, and high risk for lung cancer. Built upon Convolutional Neural Networks (CNNs) These images undergo meticulous preprocessing, including cleaning, resizing, and normalization, to prepare them for deep learning analysis. The trained model is then deployed into a user-friendly web interface, facilitating real-time predictions by healthcare professionals. Model interpretability techniques provide insights into the decision-making process, engendering trust among medical experts. This project represents a significant step forward in the early detection and diagnosis of lung cancer, with the potential to save lives through timely interventions. By integrating deep learning techniques with medical expertise, it promises to reshape the landscape of lung cancer diagnosis and patient care, ultimately contributing to improved healthcare outcomes.

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