  In recent years, electronic nose devices have become a popular approach for
identifying respiratory disorders including lung cancer. Traditional e-nose
systems have had very consistent principles and patterns of sensor responses.
After coming to the realization that detecting cancer at early stages can save
99 percent of lives, it has become imperative to design a machine that can
easily detect for lung cancer(the most common type of cancer) in a
cost-effective and accurate way. Designing an Al Nose was a perfect way to
counteract the problem. A tiny e-nose system with 14 gas sensors of four types
was created and fifty breath samples were analyzed. Five feature extraction
techniques and two classifiers were used to test the system's efficiency in
recognizing and discriminating lung cancer from other respiratory disorders and
healthy controls. Finally, the impact of different sensor types on the capacity
of e-nose systems to identify objects was investigated. The sensitivity,
specificity, and accuracy of distinguishing lung cancer patients from healthy
controls are 91.58 percent, 91.72 percent, and 91.59 percent, respectively,
when utilizing the DA fuzzy 5-NIN classification approach. The findings imply
that type-specific sensors might improve the diagnostic accuracy of e-nose
devices greatly. These findings indicated that the e-nose system described in
this work might be used in lung cancer screening with good results.
Furthermore, while creating e-nose systems, it is critical to consider
type-different sensors. This machine covers all aspects to most effectively
develop a machine that can detect lung cancer that is user-friendly and low
cost.
