AI-Assisted Computer-Aided Diagnosis for Early Detection of Lung Cancer

Abstract: Lung cancer is one of the major causes of death across the world, and early detection plays an important role in improving patient survival and treatment success. Computer-aided diagnosis (CAD) systems using CT scan images help doctors identify and classify lung nodules more effectively, supporting early-stage lung cancer diagnosis. Earlier machine learning methods such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) were widely used, but they faced difficulties in analyzing large and complex medical image data.

With the growth of deep learning, medical image analysis has improved significantly. Advanced techniques such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN) provide better accuracy in detecting, segmenting, and classifying pulmonary nodules. This review highlights recent developments in deep learning approaches for lung cancer diagnosis and explains how these methods perform better than traditional machine learning techniques.

The study also discusses the use of ensemble models and other modern approaches that increase the reliability and efficiency of pulmonary nodule analysis. Overall, deep learning has shown great potential in improving the accuracy of lung cancer detection and diagnosis. Despite some existing challenges, ongoing advancements in artificial intelligence are expected to further enhance early diagnosis and medical decision-making in the future.

Keywords: Lung cancer, Artificial Intelligence, Computer -aided diagnosis(CAD), Pumlonary nodule segmentation and classification, Deep Learning