Authors :
Sanjeevakumar M. Hatture; Madhu R. Koravanavar
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3Rjjf6L
DOI :
https://doi.org/10.5281/zenodo.6791650
Abstract :
In recent years there is heavy demand in
healthcare systems due to COVID-19 pandemic. The
COVID-19 will mainly cause the Lung infections , which
affected the whole world very badly during last two years
and still continues to affecting the world, this causes
problem to the life of common man. To overcome such
problems and also in order to identify the type of the lung
disease, and diagnosing abnormalities in the lung area the
Chest X-Rays (CXRs) and Lung Sounds are most
commonly used medical testings. Accurate identification of
diseases helps in saving the life of a human from diseases
like covid-19, pneumonia, TB, lung cancer etc. The
commonly used medical testings are cost effective and
which are very helpful in early diagnosis of pulmonary
diseases. The most difficult task for radiologists and
pulmonologists is to classify the pulmonary diseases using
images of X-rays and Lung sounds. To identify the lung
diseases, Computer Aided Diagnosis (CAD) systems assist
doctors in identifying underlying diseases. Due to less
availability of skilled radiologists and lung sound
recording devices will make the situation of the patients
more worse. The goal is to resolve the problem using non
clinical methods such as Machine and Deep Learning
Techniques and these techniques may be very helpful in
proper detection of severe respiratory diseases using lung
sounds and lung X-ray images. Lung sounds provides
better accuracy and also the proposed work provides the
precautionary measures to prevent the Lung infections.
Hence using usual medical testings and efficient techniques
are capable to overcome the severity of lung diseases. So
the work aims in identify the type of the Lung disease by
employing the machine learning techniques viz. fuzzy logic
and Convolutional neural network (CNN) in deep learning
for improvement of the performance/accuracy.
Keywords :
Chest X-rays; Lung sounds; Lung Disease Identification; CNN; Fuzzy Logic.
In recent years there is heavy demand in
healthcare systems due to COVID-19 pandemic. The
COVID-19 will mainly cause the Lung infections , which
affected the whole world very badly during last two years
and still continues to affecting the world, this causes
problem to the life of common man. To overcome such
problems and also in order to identify the type of the lung
disease, and diagnosing abnormalities in the lung area the
Chest X-Rays (CXRs) and Lung Sounds are most
commonly used medical testings. Accurate identification of
diseases helps in saving the life of a human from diseases
like covid-19, pneumonia, TB, lung cancer etc. The
commonly used medical testings are cost effective and
which are very helpful in early diagnosis of pulmonary
diseases. The most difficult task for radiologists and
pulmonologists is to classify the pulmonary diseases using
images of X-rays and Lung sounds. To identify the lung
diseases, Computer Aided Diagnosis (CAD) systems assist
doctors in identifying underlying diseases. Due to less
availability of skilled radiologists and lung sound
recording devices will make the situation of the patients
more worse. The goal is to resolve the problem using non
clinical methods such as Machine and Deep Learning
Techniques and these techniques may be very helpful in
proper detection of severe respiratory diseases using lung
sounds and lung X-ray images. Lung sounds provides
better accuracy and also the proposed work provides the
precautionary measures to prevent the Lung infections.
Hence using usual medical testings and efficient techniques
are capable to overcome the severity of lung diseases. So
the work aims in identify the type of the Lung disease by
employing the machine learning techniques viz. fuzzy logic
and Convolutional neural network (CNN) in deep learning
for improvement of the performance/accuracy.
Keywords :
Chest X-rays; Lung sounds; Lung Disease Identification; CNN; Fuzzy Logic.
