Published Paper Details:

Automatic cancer detection and segmentation is main topic for the computer-aided diagnosis of lung On the CT scans, there are abnormalities. However, because low-level visuals are too weak to recognize, it is a complex job in low-contrast photos. We propose a new technique for the automatic detection of lung Tumors in this project. We can also increase the intensity contrast of CT images by estimating the probability density function We employ expectation maximization/maximization of the posterior marginal to locate malignant spots. Finally, we use shape constraint to reduce noise and identify focal malignancies. Lung cancer is one of the leading causes of death in undeveloped nations, and early identification of the disease is difficult. Lung cancer diagnosis and treatment has been one of humanity's most difficult challenges in recent decades. Rapid recognition of malignancies would help to save a substantial number of lives all around the world on a constant schedule. This Mini Work explains how a Convolution Neural Network may also be used to distinguish lung cancers as malignant or benign.