ADVANCED METHOD FOR DETECTING CHEST DISEASE USING FEDERATED LEARNING MODELS
DOI:
https://doi.org/10.65009/7b2cv558Keywords:
Chest Diseases, Federated Learning, X-ray Image, Lymphoma disease, VGG-19, etc.,,Abstract
An important part of any nation's economy is the healthcare sector. This clearly defined
practice began a number of years ago. As time has gone on, the healthcare industry has advanced to new
heights, and technology has undoubtedly always been a major factor in this. Every stage of the evolution
of healthcare has its own set of problems. In the prehistoric age, healing with herbs and other natural
resources was a drawn-out procedure that may take years to finish because drugs had not yet been
invented.
Even if treatment times have shortened in the modern era due to technological and mechanical
advancements, storing patient data and records, records, therapies, and many other things has become
more difficult.
The detection of various chest conditions, including lymphoma disease, is the main goal of this study
project. We have used FL models, including as VGG-16, MobileNet V2, and VGG-19, to speed up and
simplify the prediction process.
Since we now know that chest diseases are so common, it is essential to properly predict and analyze
them. 112,120 chest x-ray images from the study's dataset were examined. A total of 14, including
atelectasis, consolidation, infiltration, and pneumothorax, as well as a class called "No findings" if the
condition was not discovered, were represented in the study's 30,805 images.
The highest accuracy of 96.71% was attained by VGG-19 using federated transfer learning. As a result,
the classification report said that the best transfer-learning model for correctly diagnosing chest disease
was the VGG-16 model
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