Published Paper Details:

The domain of medical image processing focuses on the early detection of lung cancer using advanced image processing techniques. Currently, lung cancer diagnosis primarily depends on manual analysis of chest X-rays or CT scans by radiologists. While experienced professionals can identify abnormalities, the process is time-consuming, prone to human error, and often limited by subtle signs that are difficult to interpret in early stages. Existing computer-aided detection systems are available, but many suffer from low accuracy, high false-positive rates, and limited generalization to diverse datasets. The system is designed to process standard datasets of lung images, performing key steps such as image preprocessing to enhance quality, feature extraction to identify relevant patterns, and classification to accurately distinguish between cancerous and non-cancerous images. The dual-phase approach consists of a training phase, where the CNN model learns from labeled data, and a testing phase, where the model is validated with unseen images to measure its performance. The application of CNNs allows the model to learn complex features that are difficult to capture through traditional handcrafted techniques, leading to improved accuracy and reduced false positives. Overall, integrating advanced image processing techniques with machine learning models such as CNN offers promising improvements in lung cancer diagnosis. This approach not only accelerates the diagnostic workflow but also supports healthcare professionals by providing consistent and reliable analysis of medical images. The proposed system has the potential to improve patient outcomes by facilitating timely detection and treatment, ultimately contributing to more effective management of lung cancer on a broader scale.