• Mrs.J.M.Waykule
• Sushmita S. Patil
• Aishwarya V. Budhe
• Rutuja R. Chavan

: Pulmonary cancer, also known as lung carcinoma, is the primary cause of cancer death worldwide. Every year, early-stage cancer detection using computed tomography (CT) could save hundreds of thousands of lives. However, analysing hundreds of thousands of these scans is a huge burden for radiologists, and they frequently experience observer tiredness, which can harm their performance. As a result, there is a requirement to efficiently read, detect, and evaluate CT scans. As a result, there is a requirement to efficiently read, detect, and evaluate CT scans. Using the centre of the lung cancer provided in the dataset, the author cropped 2D cancer masks on its reference image and trained a model with various techniques The proposed system consists of many steps such as image acquisition, pre-processing, binarization, thresholding, segmentation and feature extraction. 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 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 [6].

Gabor filter, Thresholding Approach Marker-controlled Watershed Segmentation, Binarization

"Lung Cancer Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.a384-a387, August-2021, Available :http://www.jetir.org/papers/JETIR2108047.pdf

"Lung Cancer Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppa384-a387, August-2021, Available at : http://www.jetir.org/papers/JETIR2108047.pdf