Pneumonia, a respiratory infection brought on by bacteria or viruses, affects
a large number of people, especially in developing and impoverished countries
where high levels of pollution, unclean living conditions, and overcrowding are
frequently observed, along with insufficient medical infrastructure. Pleural
effusion, a condition in which fluids fill the lung and complicate breathing,
is brought on by pneumonia. Early detection of pneumonia is essential for
ensuring curative care and boosting survival rates. The approach most usually
used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is
to develop a method for the automatic diagnosis of bacterial and viral
pneumonia in digital x-ray pictures. This article first presents the authors'
technique, and then gives a comprehensive report on recent developments in the
field of reliable diagnosis of pneumonia. In this study, here tuned a
state-of-the-art deep convolutional neural network to classify plant diseases
based on images and tested its performance. Deep learning architecture is
compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152,
Mobilenettv2, and DenseNet with 201 layers are among the architectures tested.
Experiment data consists of two groups, sick and healthy X-ray pictures. To
take appropriate action against plant diseases as soon as possible, rapid
disease identification models are preferred. DenseNet201 has shown no
overfitting or performance degradation in our experiments, and its accuracy
tends to increase as the number of epochs increases. Further, DenseNet201
achieves state-of-the-art performance with a significantly a smaller number of
parameters and within a reasonable computing time. This architecture
outperforms the competition in terms of testing accuracy, scoring 95%. Each
architecture was trained using Keras, using Theano as the backend.Comment: I have to remake the artical. Case there was some accuracy proble