• Swati Mukherjee
• Prof. Sneha Bohra

Diagnosing the patients correctly and administering treatments are a major challenge for medical practitioners as critical decisions are made based on diagnosis. The proposed framework is intended to identify lung malignancy in untimely stage in two phases. The proposed framework comprises of numerous steps, for example, image extraction, pre-processing, binarization, thresholding, Division, feature extraction, and neural system identification. In our system we will develop Lung Cancer detection system based on machine learning and neural network. It decreases the chances of getting harm to human by early detection of cancer. In recent past, there has been a lot of progress on data mining and machine learning techniques to predict the various types of diseases.The proposed model is a tool which will take input as the CT scan images and it will predict the possibilities of the disease and its stages. Thus we will try to provide a direction to medical practitioners to make quick intelligent clinical decisions which can help in prophylaxis of the disease and thereby reduce any treatment costs.

Convolutional Neural Network (CNN), Computed Tomography(CT), Support Vector Machine (SVM), Lung Cancer.

"MACHINE LEARNING APPROACH FOR LUNG CANCER DETECTION AND STAGES PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 1, page no.28-31, January-2020, Available :http://www.jetir.org/papers/JETIR2001006.pdf

"MACHINE LEARNING APPROACH FOR LUNG CANCER DETECTION AND STAGES PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 1, page no. pp28-31, January-2020, Available at : http://www.jetir.org/papers/JETIR2001006.pdf