Metaphorical Augmentation for Pulmonary Disorders Using CNN and MobileNetV2

Late diagnosis of lung cancer is a significant contributor to the overall cancer mortality rate worldwide. Cancer, an illness that is both complicated and difficult to treat, has an impact on the lives of millions of people all over the world. Early detection is critical to successful treatment and patient outcomes. Over the years, various methods have been developed to detect cancer, which include traditional imaging techniques and more advanced techniques, such as X-rays, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and molecular imaging. The use of Deep Learning and Machine Learning algorithms to evaluate medical images has been the focus of recent advancements in the detection of cancer. ML and DL can help identify anomalies in medical images, making it easier for medical professionals to detect and diagnose disease. In this study, CT scan pictures are analysed and measured using multiple assessment criteria for the Convolutional Neural Network (CNN) and MobileNetV2 algorithms to detect pulmonary diseases.