Medical imaging technologies are required for both the lung cancer early identification and continued monitoring during treatment. Computed Tomography scan images, Chest X-rays, positron emission tomography images, and other medical imaging procedures have all been extensively studied for the identification of lung cancer, magnetic resonance imaging, computed tomography, and molecular imaging methods. These methods have some drawbacks, such as the inability to automatically categorize cancer images, which makes them unsuitable for patients with other illnesses. The development of a sensitive, exact approach for detecting lung cancer in its earliest stages is desperately needed. Applications for medical image-based and textural data methods are rapidly growing. Deep learning is one of the medical imaging fields that is expanding the accelerated. The detection and classification of lung nodules can be done more rapidly and accurately by physicians using medical imaging that is based on deep learning systems. We used kaggle dataset for finding the appropriate results, and in this paper discuss the most current advancements in imaging methods relies on deep learning for the early diagnosis of lung cancer.

Computed Tomography scan Images, Lung cancer, Segmentation, Classification, Convolution Neural Network