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A Proactive Approach in Detection of Pneumonia Disease using VGG16

J. Dinesh Narayana Kumar
Dept. Of Artificial Intelligence & Data Science
KLEF Deemed to be University
Vaddeswaram, India
dineshjonnalagadda11@gmail.com
Guided By
Dr. V. Rama Krishna Professor
Dept. Of Artificial Intelligence & Data Science
KLEF Deemed to be University
Vaddeswaram, India.
Nithin Neelisetti
Dept. Of Artificial Intelligence & Data Science
KLEF Deemed to be University
Vaddeswaram, India
nithin79379@gmail.com
K. Teja Swaroop
Dept. Of Artificial Intelligence & Data Science
KLEF Deemed to be University
Vaddeswaram, India
karanamthejaswaroop@gmail.com

Abstract— Pneumonia is a life-threatening disease
caused by an infection or infection in the human lungs.
Early diagnosis of pneumonia is an important part of
effective treatment. The recent development of deep
learning, which involves many layers to understand
hierarchical data representation, has achieved the
consequences of the state of many things, especially in
the analysis and distribution of human diseases.
Therefore, to improve the performance of lung cancer
diagnosis, it is necessary to use automatic models based
on deep learning models, which can detect images at
high X-ray and facilitate lung diagnosis procedures for
novices and patients. This paper develops a neural
network (CNN) model for diagnosing lung disease
using chest X-ray images. The proposed framework has
two main stages: image preprocessing stage and feature
extraction and image classification stage. The proposed
CNN model provides high results with high
performance, recall, F1 score, and accuracy of 98%,
98%, 97%, and 99.82%, respectively. According to the
results, the proposed CNN model based on lung disease
diagnosis achieved similar and accurate results and
outperformed other previous deep learning models such
as spare part (ResNet 50) and VGG16. It also goes
beyond existing models recently mentioned in the
literature. Therefore, the significant performance of
CNN model-based lung diagnosis on all performance
measures can provide better patient care and reduce
mortality.
Keywords— deep CNN, ResNet 50, and VGG16
models