Introduction •This is the developed deep learning and AI based algorithm. •It can decrease diagnostic faults associated with lung cancer in its initial stages and sense indications of lung cancer much quicker than outdated methods. •With the help of this algorithm the medical specialist easily examine the CT scan report with a two minutes.
Methodology •Basically this methods are analysis CT images. After that this algorithm easily demonstrate it is normal or infected. •With the help of convolutional neural process the medical consultant can determine the patient’s illness. •Dataset basically stored 1000 types of CT Images which can be easily predicted by the doctors.
Approach Lung cancer is the often diagnosed cancer across the world among people. Early discovery of lung cancer routes towards appropriate medical action to save people lives. With the help of this techniques the doctors can easily detect this in an early stage.
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Impact •Theimpact of this techniques is very much high in this moment. Most of the doctors are very much for this Deep learning techniques. •The patients are also happy because the surviving rates are increasing than previously. •It is cost effective and useful for this reason many hospital authority are launching this facility.
Conclusion The lung cancer is very much serious disease for the people. Most of the cases the doctors are not penetrated this dieses efficiently. Many person are not survives for this disease. The main reason is that the doctorareanalyzeCTimagesinveryminutelyandafterthathe assumes the patients are affected by the cancer. It is very much delay process and the doctors are not able to mitigate this properly. But the modern technology are very much helpful for the medical science. The cancer detection algorithm are one example which can easily detects the difficulties of CT images. When the doctors are observing the issues then they immediately working on that. Finally most of the people are fully recover from this illness.
References Kumar, D., Wong, A., & Clausi, D. A. (2015, June). Lung nodule classification using deep features in CT images. In2015 12th Conference on Computer and Robot Vision(pp. 133-138). IEEE. Kuruvilla, J., & Gunavathi, K. (2014). Lung cancer classification using neural networks for CT images.Computer methods and programs in biomedicine,113(1), 202-209. Rubin, G. D. (2015). Lung nodule and cancer detection in CT screening.Journal of thoracic imaging,30(2), 130. Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S. J., ... & van Ginneken, B. (2016). Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks.IEEE transactions on medical imaging, 35(5), 1160-1169. Sun, W., Zheng, B., & Qian, W. (2016, March). Computer aided lung cancer diagnosis with deep learning algorithms. InMedical imaging 2016: computer-aided diagnosis(Vol. 9785, p. 97850Z). International Society for Optics and Photonics.
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