Diagnosis of Lung Cancer from Computed Tomography

In recent years, rapid advancements in technology, particularly in the realm of artificial intelligence, have significantly transformed the landscape of lung cancer diagnosis. Early detection of lung cancer is pivotal in enhancing patient outcomes; however, traditional diagnostic methods are laborious and time-consuming. Leveraging the power of deep learning techniques, specifically utilizing established neural network architectures, offers a promising solution. This study focuses on the classification of lung images from computed tomography (CT) scans into cancerous and non-cancerous categories. By employing prevalent deep learning models, transfer learning, and rigorous evaluation metrics, this study aims to assess the efficiency of these models in accurately diagnosing lung cancer. The study uses a publicly available dataset and employs preprocessing and segmentation techniques to prepare the images for analysis. The performance of the deep learning models is evaluated on the basis of parameters such as accuracy, sensitivity, specificity, and F1 score. The results demonstrate remarkable accuracy rates, with specific architectures such as ResNet-152V2 and the proposed deep convolutional neural network architecture achieving a staggering 99.1% accuracy. These findings underscore the potential of deep learning techniques in revolutionizing lung cancer diagnosis, offering valuable support to healthcare professionals, and paving the way for more efficient and accurate diagnostic practices.