• Abhijeet Shimple
• Rachit Sardar
• Shivam Raj
• Yash Badodekar

Detection of lung cancer is the most interesting research area of researcher's in early stages. The proposed system is designed to detect lung cancer in premature stage in two stages. The proposed system consists of many steps such as image acquisition, preprocessing, binarization, thresholding, Segmentation, feature extraction, and neural network detection. At first Input lung CT images to the system and then passed through the image preprocessing stage by using some image processing techniques. In first stage, Binarization technique is used to convert binary image and then compare it with threshold value to detect lung cancer. In second stage, segmentation is performed to segment the lung CT image and a strong feature extraction method has been introduced to extract the some important feature of segmented images. Extracted features are used to train the neural network and finally the system is tested any cancerous and noncancerous images. The Lung cancer is a disease of abnormal cells multiplying and growing into a tumor. Cancer cells can be carried away from the lungs in blood, or lymph fluid that surrounds lung tissue. Now several systems are proposed and still many of them are conceptual design. Efficient Net based Classification and detection system of lung cancer.

Conventional Neural Network (CNN), Computed Tomography, Efficient Net, and Small Cell Lung Cancer.

"“Lung Cancer Detection”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 3, page no.e284-e286, March-2022, Available :http://www.jetir.org/papers/JETIR2203435.pdf

"“Lung Cancer Detection”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 3, page no. ppe284-e286, March-2022, Available at : http://www.jetir.org/papers/JETIR2203435.pdf