Scribd
Upload
23 views2 pages

Proposal 2 (AI)

The document discusses the development of an AI-enabled system aimed at the early detection of lung cancer using deep learning techniques. It highlights the limitations of existing research and proposes a framework that incorporates multiple CNN architectures and patient descriptive statistics to enhance prediction accuracy. The objectives include improving classification performance through a novel feature selection algorithm and combining various models for better outcomes in lung cancer detection.

Uploaded by

jyothi
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
23 views2 pages

Proposal 2 (AI)

The document discusses the development of an AI-enabled system aimed at the early detection of lung cancer using deep learning techniques. It highlights the limitations of existing research and proposes a framework that incorporates multiple CNN architectures and patient descriptive statistics to enhance prediction accuracy. The objectives include improving classification performance through a novel feature selection algorithm and combining various models for better outcomes in lung cancer detection.

Uploaded by

jyothi
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
You are on page 1/ 2

An AI Enabled System for Automatic and Early

Detection of Lung Cancer

ABSTRACT

With recent advances in deep learning, research has made a significant leap to help
identify, classify, and quantify patterns in medical images. Particularly, improvements in
computer vision inspired its use in medical image analysis such as image segmentation,
image registration, image fusion, image annotation, computer-aided diagnosis and prognosis,
lesion/landmark detection, and microscopic imaging analysis, to name a few. Particularly
lung cancer is one of the problems that has attracted significant research. Many deep learning
based solutions came into existence. However, there is lot to do to have more accurate
detection of lung cancer early. For instance, the research carried out in [1] has significant
limitations. First, it has issues in detection of lung cancer early. Second, there is need for
improving CNN architectures and cascade them besides making a pipeline with patient-level
descriptive statistics for better prediction. Third, ensemble of classifiers could lead to further
improvement in prediction of lung cancer. Based on these findings in the literature, the
follow section provides research aim and objectives.

Aim and Objectives

The aim of the research is to develop a deep learning based framework with multiple
CNN architecture pipelined with patient’s descriptive statistics to leverage state of the art in
prediction of lung cancer early. The following are the objectives.

1. To propose a deep learning framework with multiple CNN architectures and a hybrid
algorithm to improve accuracy in early detection of lung cancer.
2. To enhance the framework by defining a novel feature section algorithm that
improves performance of the classifiers.
3. To enhance the framework further by combining feature selection and CNN models
besides using patients’ descriptive statistics to improve performance in early detection
of lung cancer.
Reference

[1] S.K., L., Mohanty, S. N., K., S., N., A., & Ramirez, G. (2018). Optimal deep learning
model for classification of lung cancer on CT images. Future Generation Computer
Systems. doi:10.1016/j.future.2018.10.009

You might also like