Deep Learning Algorithm for Lung Cancer Detection in CT Images

This project presents a deep learning and AI-based algorithm designed to improve the early detection of lung cancer. The algorithm analyzes CT images to differentiate between normal and infected tissues, enabling medical specialists to quickly assess patient conditions within minutes. Utilizing convolutional neural networks and a dataset of 1000 CT images, the algorithm aims to reduce diagnostic errors and facilitate earlier medical intervention. The impact of this technology is significant, with increasing survival rates and cost-effectiveness leading to its adoption in hospitals. By addressing the limitations of traditional CT image analysis, this AI algorithm offers a promising solution for enhancing lung cancer diagnosis and patient outcomes.

Lung Cancer detection algorithm

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stages and sense indications of lung cancer much quicker than outdated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.

Impact
• The impact 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
doctor are analyze CT images in very minutely and after that he
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. In 2015 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. In Medical
imaging 2016: computer-aided diagnosis (Vol. 9785, p. 97850Z).
International Society for Optics and Photonics.

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