Performance Analysis of Various Deep Learning Models in Lung Cancer Detection and Classification Using Medical Images

Lung cancer remains a prevalent and deadly disease, accounting for millions of deaths globally each year. Early detection and accurate diagnosis of lung cancer are pivotal in improving patient outcomes and survival rates. Traditional diagnostic methods often face limitations in sensitivity and specificity, necessitating the exploration of advanced technological solutions. This research paper explores the application of deep learning models, specifically Convolutional Neural Networks (CNNs), ResNet50, MobileNetV2 and VGG-16 architectures, in the detection and classification of lung cancer from medical imaging data. The primary objective of this study is to develop and evaluate deep learning-based models capable of identifying lung cancer patterns in radiological images, notably X-rays and CT scans. These models leverage the inherent capacity of deep learning to automatically extract intricate features and patterns from complex image data, aiding in the precise identification of cancerous regions. The paper reviews the strengths and limitations of existing approaches examining their efficacy in identifying and classifying lung nodules in achieving reliable and robust results. Furthermore, it addresses the challenges associated with utilizing deep learning in medical diagnosis, such as data scarcity, bias, and interpretability. Through meticulous analysis and experimentation, we aim to evaluate the potential of deep learning models to revolutionize lung cancer detection, paving the way for improved patient outcomes and early intervention strategies.