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Lung cancer is one of the most prevalent cancer-related diseases with a high mortality rate, and this is largely due to the lateness in detecting the presence of malignancy. Again, the conventional methods used in the diagnosis of lung cancer have had their shortfalls. While the effectiveness of computerized tomography in detecting this malignancy, the large volumes of data that radiologists must process not only present an arduous task but may also slow down the process of detecting lung cancer early enough for treatment to take its course. On a global scale, lung cancer is noted to be one of the most prevalent cancer-related diseases with a high mortality rate. The prognosis of the disease has not been very favorable and this is largely due to the lateness in detecting the presence of malignancy. It is more necessary for care to immediately & correctly examine the lung cancer nodules.The various Machine learning models have been utilized in the medical field for quite a while now, and as it has displayed its many strengths, so could the limitations not be hidden. It is in addressing these limitations and improving on the detection prowess of the convolutional neural network that the 3D model is now fast gaining attraction, it has motivated us. When there several methods used to examine lung cancer ,deriving towards CT scan images as CT Scan images are powerful and it takes images of internal organs, vertebrae, and the spine in 360 degrees which gives a more clear view. In this paper, we are using 3D Convolutional Neural Network (CNN) for identification of lung cancer from the Computed Tomography (CT) scans of the patient, since CNN makes it easier to obtain the important information from the images. This paper focuses on developing a 3-Dimensional Convolutional Neural Network that Detects whether the CT Scan Image is Cancerous or Non-Cancerous. Therefore, an automated method that can determine whether the patient will be diagnosed with lung cancer is the aim of this paper. |
