AUTOMATED LUNG CANCER DETECTION USING NAS: A HIGH-PERFORMANCE DEEP LEARNING APPROACH

Abstract – Lung cancer remains one of the leading causes of mortality worldwide, necessitating early and accurate detection for effective treatment. This study explores the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Neural Architecture Search (NAS), for automated lung cancer detection from CT scan images. CNNs, while effective, often require manual architecture tuning, leading to suboptimal performance. NAS, on the other hand, optimizes network architecture automatically, resulting in improved accuracy. Experimental results demonstrate that CNN achieves an accuracy of 84.38%, whereas NAS significantly outperforms it with an accuracy of 96.35%. The superior performance of NAS is attributed to its ability to discover the most efficient network structure tailored to lung cancer detection. These findings highlight the potential of automated deep learning approaches in medical image analysis, contributing to more reliable and precise diagnostic tools. Keywords: Lung Cancer Detection, Deep Learning, Neural Architecture Search (NAS), Convolutional Neural Networks (CNN).