Lung Cancer Detection using Deep Learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, primarily due to latediagnosis and limited early detection methods. Recent advancements in deep learning haverevolutionized medical image analysis, offering promising solutions for early and accurate cancerdetection. This study presents a deep learning-based approach for automated lung cancer detection usingconvolutional neural networks (CNNs). The proposed model is trained on a dataset of computedtomography (CT) scan images, leveraging image preprocessing, augmentation, and feature extractiontechniques to improve detection accuracy.Our experimental results demonstrate high sensitivity and specificity in distinguishing malignant frombenign lung nodules, outperforming traditional machine learning approaches. The implementation of thisdeep learning framework has the potential to assist radiologists in early diagnosis, reducing human errorand improving patient outcomes.