A Data-Driven Approach to Detecting Lung Cancer with Smart VGG19 Algorithm

One of the main causes of cancer-related deaths, lung cancer, requires early detection in order to be effectively treated. Our proposal is an automated method that evaluates CT data using machine learning to assist radiologists in accurately identifying troublesome lung nodules. The method focuses on nodule features via pre-processing CT images. Annotated CT scans are mostly used to train machine learning models, like CNNs, to recognize patterns of benign and cancerous nodules. To find the best effective model for detection, several CNN architectures are tested, such as ResNet, DarkNet, and EfficientNet. We present the MMT-VIN Basic model, which is based on the VGG-19 methodology. With an accuracy of almost 97.58%, the suggested lung cancer detection method outperforms the techniques it was compared to.