Advancements of Deep Learning-Based Pneumonia Chest
Classification
Yupeng Tong
a
Computer Science, University of Macau, Macao, China
Keywords: Pneumonia, Machine Learning, Artificial Intelligence.
Abstract: Pneumonia, a severe respiratory illness with high mortality and morbidity rates, requires early and accurate
diagnosis to ensure timely treatment. This paper explores the application of deep-learning techniques for
pneumonia chest classifying based on medical image modalities such as X rays and Computed Tomography
(CT) scanners. The methodology includes a framework for deep-learning-based pneumonia chest
classification, which includes data collection, preprocessing and model development. The study uses a variety
of deep learning architectures including Convolutional Neural Networks, Artificial Neural Networks, and
Vision Transformers. The dataset is a large collection of chest X-rays and CT images that are preprocessed to
improve model performance. This dataset is used to train deep learning models using advanced techniques
like transfer learning, data enhancement, and architectural improvements. The performance of the model is
evaluated with appropriate metrics and techniques such as SHapley Additive exPlanations (SHAP) are used
to enhance interpretability. And the deep-learning techniques’ application for pneumonia chest classification
has shown promising results in terms of accuracy and efficiency. The study highlights the importance for deep
learning in the area such as pneumonia classification and stresses the importance of addressing limitations to
enable practical implementation.
1 INTRODUCTION
Pneumonia, a common respiratory illness, is
characterized by inflammation of the lung tissue and
infection. Early diagnosis and accurate classification
are essential for the prevention and treatment of
complications. Chest imaging such as chest
Computed Tomography (CT) scans and chest X-rays
are vital in the diagnosis and classifying of
pneumonia (World Health Organization 2022; Zhang
2022). In recent years there has been an increasing
interest in using machine learning and Artificial
Intelligence techniques to improve the accuracy of
pneumonia chest classification. These advanced
techniques have the potential to automate
classification, reduce human errors, and help
healthcare professionals make more informed
decisions in medical-related diagnosis (Lambert,
2024; Qiu, 2019; Qiu, 2022). Several studies have
explored the application of machine-learning
algorithms in pneumonia chest classifying. Smith
developed a deep-learning model that was highly
a
https://orcid.org/0009-0008-7683-0728
accurate in identifying bacterial pneumonia from
viral pneumonia using chest X ray images (Smith,
2021). The model used Convolutional Neuronal
Networks (CNNs), which extracted meaningful
features from the images to classify them. The results
showed a promising potential for accurate
classification of pneumonia, which could help in
selecting the appropriate treatment. A study by
Johnson focused on the differentiation between
Community-Acquired pneumonia (CAP) and
Hospital-Acquired Phthisis (HAP) utilizing deep
learning techniques (Johnson, 2023). The researchers
trained a neural network using a large dataset chest
CT scan and achieved excellent results in
distinguishing CAP from HAP cases. This
classification is important because the treatment and
management strategies for these two types differ.
Moreover, studies have also delved into
combining information, with imaging characteristics
to enhance the classification of pneumonia. For
example, in a study by Li et al. a hybrid model was
created that merged features from chest X rays with
564
Tong, Y.
Advancements of Deep Learning-Based Pneumonia Chest Classification.
DOI: 10.5220/0012959300004508
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 564-568
ISBN: 978-989-758-713-9
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
patient demographics and laboratory test results (Li,
2023). This integration notably boosted the accuracy
of pneumonia classification in cases where imaging
results were inconclusive.
Classifying pneumonia based on chest imaging
plays a role in settings as it aids in selecting
appropriate treatments and managing patients
effectively. The utilization of machine learning and
AI methods holds potential in refining the precision
and speed of pneumonia classification. Deep learning
models like CNNs have shown capabilities in
distinguishing types of pneumonia through chest
imaging analysis. Furthermore, incorporating data
with imaging features has further improved the
accuracy of classification. Given its significance and
rapid advancements a thorough examination of this
field is imperative.
The paper is structured as follows; Section 2
outlines the methodologies employed. Section 3
presents discussions and Section 4 concludes the
paper.
2 METHOD
2.1 Framework of Deep Learning-
Based Pneumonia Chest
Classification
A deep learning framework for pneumonia chest
classifiers typically includes data collection and
preprocessing, as well as model building and training.
Data collection involves collecting chest X-ray
images from various sources including hospitals and
research databases. The dataset contains cases of viral
and bacterial pneumonia as well as healthy controls.
2.2 Convolutional Neural Network
In their study, Rajpurkar et al. proposed a novel CNN
architecture specifically designed for pneumonia
chest classification (Rajpurkar, 2017). Their model,
named CheXNet, incorporated residual connections
and densely connected blocks to enhance feature
extraction and classification performance. The use of
residual connections helped alleviate the vanishing
gradient problem and facilitated the flow of gradients
during training.
In another study by Wang et al. (Wang, 2018), a
CNN model with attention mechanisms was proposed
for pneumonia chest classification. The special
mechanisms enabled the model to focus its attention
on the most informative areas of the chest X ray
images, improving classification accuracy.
2.3 Artificial Neural Network
Artificial Neural Networks (ANN), a model of
computation inspired by the nervous system of the
human brain (Li, 2024; Liu, 2023). It is made up of a
series of artificial neurons (also called nodes or units),
which are interconnected and transfer information via
weights. The applications of neural networks are
numerous, including speech and image recognition,
natural language processing and machine translation.
It uses adaptive and nonlinear models to learn from
large data sets and discover patterns in the data. Shen
et al. (Shen, 2019) developed an ANN for pneumonia
chest classification. Their model used a multilayer
architecture perceptron with multiple hidden layers.
The ANN was optimized using a backpropagation
method to optimize its weights and biases
.
Similarly, Li et al. proposed a different ANN
architecture for pneumonia chest classification (Li,
2018). Their model incorporated batch normalization
and dropout regularization techniques to prevent
overfitting and improve generalization performance.
2.4 Vision Transformer
Dosovitskiy et al. introduced the Vision Transformer
(ViT), a deep learning architecture that has shown
promising results in image classification tasks
(Dosovitskiy, 2021). While initially designed for
natural images, researchers have also explored its
application in medical imaging, including pneumonia
chest classification. The ViT model utilizes self-
attention mechanisms to capture global and local
relationships within the images, enabling effective
feature representation.
Another researcher, Zhang et al. also highlighted
the effectiveness of the ViT architecture in the field
of video understanding (Zhang, 2023). By leveraging
self-attention mechanisms, the ViT model captures
both spatial and temporal relationships within video
frames, enabling robust feature representation and
facilitating accurate video classification. This
extension of the ViT model to the domain of video
understanding opens up new possibilities for
applications in action recognition, video
summarization, and surveillance systems.
Compared with traditional CNN, ViT adopts a
global attention mechanism in image processing by
dividing the image into a series of image patches and
feeding them into the Transformer model as a
sequence for processing. It is a powerful model for
handling large-scale images with modularity and
scalability. Its modular design allows for easy scaling
Advancements of Deep Learning-Based Pneumonia Chest Classification
565
and customization by adjusting the number of
Transformer layers. It also demonstrates cross-
domain migration and generalization capabilities,
enabling knowledge transfer to different tasks or
domains through fine-tuning. However, challenges
exist, such as the need for additional spatial encoders
for images with rich spatial information and potential
performance degradation with small-scale images
compared to CNN models.
In summary, the methodology for pneumonia
chest classification involved the collection and
preprocessing of a large dataset, followed by the
development and training of deep learning models.
These models incorporated various architectural
enhancements to improve classification accuracy and
performance. Overall, the methodology for
pneumonia chest classification employed a
systematic approach that encompassed data
collection, preprocessing, model development, and
training. Further research and advancements in this
field will continue to refine and expand upon these
methodologies, leading to improved diagnosis and
treatment of pneumonia.
3 DISCUSSIONS
Pneumonia chest classification using machine
learning and AI techniques has shown great potential
in improving accuracy and efficiency. However, there
are several limitations and challenges that need to be
addressed. In this section, this paper will discuss these
limitations and challenges, and explore future
prospects and possible solutions.
3.1 Interpretability
One significant hurdle in using learning models for
categorizing pneumonia in chest X rays is the lack of
interpretability. Models like CNNs and ViTs are often
seen as enigmatic because they derive patterns from
data without offering explanations for their choices.
This opacity can impede the acceptance of models in
settings, where understanding and openness are vital
(Carneiro, 2017).
A potential remedy involves developing
techniques to make deep learning models more
interpretable. Approaches like SHapley Additive
exPlanations (SHAP) can be utilized to pinpoint the
features or areas in images that influence the
classification outcome. By incorporating
interpretability techniques healthcare professionals
can grasp how these models make decisions and build
confidence in their predictions.
3.2 Applicability
One issue that arises is how well the models
developed can be used in settings. The effectiveness
of learning models heavily depends on having access
to varied datasets. However, gathering datasets
containing labeled cases of pneumonia from hospitals
and medical facilities can pose challenges due to
privacy issues and restrictions on sharing data. This
limited data availability could impact the ability of
the models to generalize and perform well (Lakhani,
2017).
To address this challenge transfer learning can be
applied by utilizing trained models from large image
datasets like ImageNet and adjusting them for
pneumonia chest X ray images. This strategy can help
mitigate the constraints posed by limited labeled
pneumonia data and enhance model performance.
Ongoing research focusing on developing model
structures tailored specifically for classifying
pneumonia in chest X rays has the potential to boost
accuracy and efficiency.
Further exploration of improvements, such as
attention mechanisms could aid in capturing
meaningful image features and improving
classification accuracy. Within transfer learning
domain adaptation techniques offer an avenue for
enhancing model performance when there are
discrepancies between training data and real-world
data. Adapting the model to suit the target domain can
bolster both generalization capabilities and accuracy,
in classifying pneumonia in chest X rays.
3.3 Privacy
Privacy is a crucial concern when dealing with
medical data, including chest X-ray images. The use
of deep learning models requires access to large
datasets, which may contain sensitive patient
information. Ensuring patient privacy and
maintaining data confidentiality is of utmost
importance to comply with ethical and legal
regulations (Wang, 2018).
Federated learning is an emerging approach that
enables training of machine learning models on
decentralized data sources without sharing the raw
data. In the context of pneumonia chest classification,
federated learning can be applied to train models
using data from multiple hospitals or medical centers,
while keeping the data localized and secure. This
approach allows for collaborative model training
while preserving data privacy. In addition, the
hardware situation and transmission mechanisms
should be also improved to combine with federated
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learning algorithms well (Deng, 2019; Deng, 2023;
Sugaya, 2019).
4 CONCLUSIONS
Machine learning and artificial intelligence
techniques have shown significant potential to
improve accuracy and efficiency in the classification
of pneumonia chest. Deep learning models have been
used successfully to distinguish between different
types of pneumonia based on chest images. To ensure
that these models can be used in clinical settings,
several challenges and limitations must be addressed.
Interpretability is a major issue, as deep-learning
models lack explicit explanations of their decisions.
SHAP is one method that can be used to improve
interpretability and gain insights into the decision-
making process. Deep learning models are proving to
be difficult to apply in clinical settings, particularly in
the classification of chest images for pneumonia. The
availability of large, diverse datasets is a key factor
for model performance. However, collecting these
datasets can be difficult due to privacy and sharing
restrictions. This can have an impact on the
generalization and performance of the model.
Transfer learning can be used to overcome this
problem. Models pre-trained using large-scale image
databases such as ImageNet can then be fine-tuned to
fit pneumonia chest images. When dealing with
medical data privacy is essential. Federated learning
provides a solution to this problem by allowing model
collaboration without sharing raw data.
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