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ABSTRACT
Pneumonia is one of the serious diseases that is caused by a bacterial or viral infection
of the lungs and has the potential to result in severe consequences within a short
period. Therefore, early diagnosis is a key factor in terms of the successful treatment
process. Thus, there is a need for an intelligent and automatic system that has the
capability of diagnosing chest X-rays and to simplify the pneumonia detection process
for experts as well as for novices. This study aims to develop a model that will help with
the classification of chest x-ray medical images into normal(healthy) vs abnormal(sick).
To achieve this, seven existing state-of-the-art machine learning techniques and well-
known convolutional neural network models have been employed to increase efficiency
and accuracy. In this study, we propose our machine learning for the classification task,
which is trained with modified images, through multiple steps of preprocessing.
Experimentally, it demonstrated that the machine learning technique for the
classification task performs the best, compared to the other seven machine learning
techniques. In this study, we successfully classified chest infection in chest predict
using on CNN with an overall accuracy of 98.46%. It achieved a more successful result
in detecting pneumonia cases. Machine learning models. In this study, we propose a
method for image-based diagnosis for Pneumonia leveraging deep learning techniques
and interpretability of explanation models such as Local Interpretable Model-agnostic
Explanations and Saliency maps. We experiment on a variety of sizes and
Convolutional neural network architecture to evaluate the efficiency of the proposed
method on the set of Chest x-ray images. The work is expected to provide an approach
to distinguish between healthy individuals and patients who are affected by Pneumonia
as well as differentiate between viral Pneumonia and bacteria Pneumonia by providing
signals supporting image-based disease diagnosis approaches. . This study aims to
develop a model that will help with the classification of chest x-ray medical images into
normal(healthy) vs abnormal(sick). To achieve this, seven existing state-of-the-art
machine learning techniques and well-known convolutional neural network models have
been employed to increase efficiency and accuracy. In this study, we propose our deep
learning for the classification task, which is trained with modified images, through
multiple steps of preprocessing. Experimentally, it demonstrated that the deep learning
technique for the classification task performs the best, compared to the other seven
machine learning techniques. In this study, we successfully classified chest infection in
chest Xray images using deep leaning based on CNN with an overall accuracy of
98.46%. It achieved a more successful result in detecting pneumonia cases.
