Deep Learning for Automated Detection of Lung Cancer from Medical Imaging Data

Early and accurate detection of lung cancer is essential for improving patient outcomes and reducing mortality rates. On a range of medical image processing applications in recent years, deep learning techniques have demonstrated exceptional effectiveness. This study used medical imaging data, specifically CT scans and chest X-rays., to conduct a thorough research into the use of deep learning for automated lung cancer diagnosis. The early and accurate detection of lung cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, deep learning techniques have demonstrated remarkable performance in various medical image analysis tasks. This research paper presents a comprehensive study on the application of deep learning for the automated detection of lung cancer from medical imaging data, primarily focusing on chest X-rays and computed tomography (CT) scans. We explore different deep learning architectures, data preprocessing techniques, and training strategies to enhance the accuracy and efficiency of lung cancer detection. Through rigorous experimentation and validation on diverse datasets, we showcase the potential of deep learning models in identifying lung cancer During its initial phases. The findings highlight the significance of implementing effective data augmentation techniques., transfer learning, and model interpretability in achieving reliable and clinically applicable outcomes. This study contributes to the ongoing efforts in harnessing the power of deep learning for effective lung cancer diagnosis and establishes a foundation for further advancements in the field of medical image analysis.