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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM36824
Volume:
8 |
7 |
Jul 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The process of classifying; detecting and measuring the certainty of lung x-ray image’s abnormality using enhanced complex and deep learning of techniques such as CNN has observed improvements betterment especially to enhance a working solution i.e. pneumonia ,tuberculosis ,COVID-19,lung cancer. With regard to the datasets, several have been applied in the testing of the model to the utmost extent of its ability as well as its accuracy was well received in passing on the message. Also an intuitive graphical user interface makes it possible for physicians/managers to evaluate x-ray pictures within a short span of time along with exact outcomes linked to CNN output. The goal is to incorporate brilliant algorithms of deep learning into clinical practice to develop a more efficient diagnostic criterion of ailments that affect the chest, early detection and treatment, various treatment plans tailored according to the patient’s needs so as to enhance their quality of life.
KEYWORDS
Publication Date
7/26/2024
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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