Enhancing Early Detection of Lung Cancer Through Advanced Medical Imaging and Automation: A Focus on CT scans

Lung cancer remains the leading cause of cancer-related deaths globally, highlighting the need to focus on early detection and prognosis. This research paper addresses the significant challenges arising from a shortage of radiologists and their heavy workloads, which hinder the prompt and accurate analysis of medical imaging data. In response, our study centers on developing automated methods for accurately predicting the progression of lung cancer by analyzing medical imaging data. The paper offers a comprehensive analysis that covers various aspects of lung cancer detection, including datasets and image processing techniques. Furthermore, this research expands its scope to address broader issues related to lung cancer and presents potential solutions. Importantly, it emphasizes the critical role of CT scans in precisely identifying and diagnosing lung cancer. This work also presents the techniques for stationary wavelet transform (SWT) and discrete wavelet transform (DWT), emphasizing how they can be combined to increase the precision of lung cancer detection using medical imaging analysis. This research combines advanced techniques in medical imaging and automation to transform the early detection of lung cancer, paving the way for faster and more precise diagnostic procedures.