~his invention presents a novel and efficient method for the automated detection and
Fiassification of pneumonia using deep leaming algorithms, specifically Conv.olutional Neural
!Networks (CNNs), applied to chest X-ray images. Pneumonia is a life-threatening condition
Fharacterized by inflammation of the lungs, primarily due to bacterial, viral, or fungal infections.
It results in fluid or pus accumulation in the alveoli, leading to breathing difficulties, reduced
pxygen exchange, and severe systemic compl·i<.:alious if left untreated. Traditional diagnosis rclic5
pn clinical examination, radiologic imaging, and laboratory testing, but these methods are often
ime-intensive and subject to human error, especially in busy or under-resourced healthcare
~ettings.
~his invention addresses these limitations by leveraging the power of Al-based image recognition
~ystems. A deep learning model was developed and trained using a curated dataset of 20,00U
Fhest X-ray images, each standardized to a resolution of 224x224 pixels. The dataset includes
labeled cases of bacterial pneumonia, viral pneumonia (including COVID-19), and healthy lungs.
Using a batch size of32, the CNN was trained to recognize patterns ,and ~eatures associated with
~ifferent pneumonia types, achieving a· classification accuracy of95'%. The proposed system can
~ccurately identifY the presence of pneumo11ia and differentiate between its bacterial and viral
iforms. This has substantial clinical value, as treatment protocols vary significantly depending on
he pneumonia type. For instance, antibiotics are effective against bacterial pneumonia but are
rot suitable for viral infections. Early and accurate classification enables healthcare providers tc
initiate the correct treatment promptly, improving recovery rates and reducing mortality. The
~NN architecture used in this invention includes convolutional layers for feature extraction

Advanced optimization techniques such as data augmentation, dropout regularization, and
learning rate adjustments were employed t.o enhance the model's generalization and reduce
pverfitting. Beyond the technical framework, the invention contributes to the advancement o
FOmputer-aided diagnostic (CAD) tools and intelligent healthcare systems. The model can be
integrated into radiology software, hospital management systems, or even mobile devices to
provide quick second opinions, especially in remote locations lacking access to trained
radiologists. It also supports telemedicine by allowing frontline healthcare workers to upload X
ray images and receive diagnostic feedback in real-time. The invention supports scalable
~eployment and has the potential to screen large volumes of patient data efficiently, reducing
~iagnostic delays in high-patient-load environments such as during pandemics. It is especially
~aluable in global health scenarios where pneumonia remains a leading cause of death,
particularly among children and the elderly in low-income regions. In conclusion, this inventior
pffers an innovative and practical Al-driven solution for pneumonia diaguosis. By combining tlu
~trengths of deep learning with the diagnostic power of radiography, it significantly enhances
~ccuracy, speed, and accessibility of pneumonia detection, setting a foundation for broade
~pp!ications in medical imaging diagnostics.

Background ofthe Invention
Pneumonia remains a critical public health concern worldwide, particularly
affecting children, the elderly, and immunocompromised individuals. According
to the World Health Organization (WHO), pneumonia is responsible for over 2.5
million deaths annually, including more than 700,000 children under the age of
five. The condition is characterized by inflammation and infection in the alveoli
of the lungs? which fill with pus or fluid, causing difficulty in breathing, chest ·
pain, fever, and fatigue. Bacterial infections, most commonly Streptococcus
pneumoniae, are among the leading causes, although viral agents such as
influenza and SARS-CoV-2 have also contributed to recent global surges in
pneumonia cases. Early and accurate diagnosis is essential for effective treatment.
Conventionally, pneumonia is diagnosed through clinical examination and
imaging techniques such as chest X-rays, computed tomography (CT) scans, and
ultrasound. However, misinterpretation of medical images by radiologists can
result in delayed or incorrect treatment. A study published in the journal
Radiology revealed that human diagnostic accuracy in interpreting chest X-rays
ranges between 70% and 80%, depending on expertise and workload.
With the rise of artificial intelligence (AI) and deep learning, particularly
Convolutional Neural Networks, (CNNs), there has been a significant leap in
medical image analysis. CNNs have demonstrated exceptional capability in
pattern recognition and classification tasks in radiology. By leveraging large
datasets and deep architectures, AI can now assist clinicians in making more
accurate, faster, and unbiased diagnostic decisions, thereby reducing mortality
and improving patient outcomes in pneumonia cases.
Field of Invention
The present invention falls within the interdisciplinary field of medical
diagnostics, with a specific focus on the application of artificial intelligence (AI)
and deep learning techniques in radiology. It addresses the growing need for
accurate, fast, and reliable detection of pneumonia through automated analysis of
chest X-ray images. The invention merges the domains of healthcare, computer
vision, biomedical engineering, and machine learning, aiming to enhance clinical
decision-making and reduce human error in diagnostic procedures.
Pneumonia, as an infectious disease affecting lung tissue, often necessitates
imaging-based diagnosis for appropriate clinical intervention. Traditional
diagnostic methods, including physical examination and radiographic assessment
by medical experts, are time-consuming and prone to variability in interpretation.
The field of invention thus explores the integration of Convolutional Neural
Networks (CNNs)-a specialized type of deep learning algorithm known for its
ability to process visual data-with digital radiography for early-stage pneumonia
detection.
In recent years, AT has shown transfonnative potential in healthcare by enabling
systems to learn from medical datasets, recognize patterns, and make predictions
with high accuracy. CNNs, in particular, have revolutionized medical imaging by
providing tools that can detect subtle anomalies in X-ray scans, often surpassing
the diagnostic capabilities of non-specialist physicians.
This invention contributes to the broader tield of computer-aided diagnosis
(CAD) systems and intelligent healthcare technologies. It offers a scalable and
cost-effective solution for healthcare settings with limited access to radiologists,
especially in rural or under-resourced regions. Furthermore, it opens up pathways
for real-time monitoring, triage assistance in emergency situations, and
integration into telemedicine platforms. By focusing on pneumonia, including
bacterial, viral, and COVTD-19-induced cases, the invention holds significant
promise for improving patient outcomes and optimizing healthcare workflows.
Description of the Invention
This invention presents a novel and efficient method for the automated detection
and classification of pneumonia using deep learning algorithms, specifically
Convolutional Neural Networks (CNNs), applied .to chest X-ray images.
Pneumonia is a life-threatening condition characterized by inflammation of the
lungs, primarily due to bacterial, viral, or fungal infections. It results in fluid or
pus accumulation in the alveoli, leading to breathing difficulties, reduced oxygen
exchange, and severe systemic complications if left untreated. Traditional
. diagnosis relies on clinical examination, radiologic imaging, and laboratory
testing, but these methods are often time-intensive and subject to human error,
especially in busy or under-resourced health care settings.
This invention addresses these limitations by leveraging the power of AI-based
image recognition systems. A deep learning model was developed and trained
using a curated dataset of 20,000 chest X-ray images, each standardized to a
resolution of 224x224 pixels. 'fhe dataset includes labeled cases of bacterial
pneumonia, viral pneumonia (including COVID-19), and healthy lungs. Using a
batch size of 32, the CNN was trained to recognize patterns and features
associated with different pneumonia types, achieving a classification accuracy of
95%. The proposed system can accurately identifY the presence of pneumonia
and differentiate between its bacterial and viral forms. This has substantial
clinical value, as treatment protocols vary significantly depending on the
pneumonia type. For instance, antibiotics are effective against bacterial
pneumonia but are not suitable for viral infections. Early and accurate
classification enables healthcare providers to initiate the correct treatment
promptly, improving recovery rates and reducing mortality. The CNN
architecture used in this invention includes convolutional layers for feature
extraction, pooling layers to reduce spatial dimensions, and fully connected layers
for classification. Advanced optimization techniques such as data augmentation,
dropout regularization, and learning rate adjustments were employed to enhance
the model's generalization and reduce overfitting. Beyond the technical
framework, the invention contributes to the advancement of computer-aided
diagnostic (CAD) tools and intelligent healthcare systems. The model can be
integrated into radiology software, hospital management systems, or even mobile
devices to provide quick second opinions, especially in remote locations lacking
access to trained radiologists. It also supports telemedicine by allowing frontline
healthcare workers to upload X-ray images and receive diagnostic feedba<.:k in
real-time. The invention supports scalable deployment and has the potential to
screen large volumes of patient data effiCiently, reducing diagnostic delays in
high-patient-load environments such as during pandemics. It is especially
valuable in global health scenarios where pneumonia remains a leading cause of
death, particularly among children and the elderly in low-income regions. In
conclusion, this invention offers an innovative and practical AI-driven solution
for pneumonia diagnosis. By combining the strengths of deep learning with the
diagnostic power of radiography, it significantly enhances accuracy, speed, and
accessibility of pneumonia detection, setting a foundation for broader
applications in medical imaging diagnostics ..
I. An artificial intelligence-bao.;ed system for detecting pneumoma,
compnsmg
a Convolutional Neural Network (CNN) model trained on a plurality of
labeled chest X-ray images, wherein the model classifies input images into
categories comprising bacterial pneumonia, viral pneumonia (including
COVID-19), and normal lungs.
2. ·The system of claim I, wherein the CNN model is trained using a dataset
of at least 20,000 chest X-ray images with a uniform resolution of224x224
pixels, enabling high-resolution feature extraction and consistent model
input processing.
3. The system of claim 1, wherein the model achieves a classification
accuracy of at least 95% on validation data through the application of
optimization techniques including data augmentation, dropout
regularization, and learning rate scheduling.
4. A method for diagnosing pneumonia using chest X-ray images,
comprising the steps of:
(a) receiving a digital chest X-ray image as input;
(b) preprocessing the image to a standardized resolution;
(c) feeding the image into a trained CNN model;
(d) outputting a diagnostic classification indicating presence and type of
pneumonia or a normal result.
5. The method of claim 4, further comprising integrating the trained model
into a clinical decision support system, enabling real-time diagnostic
assistance to medical practitioners.
6 .. The system of claim I, wherein the model is deployable on cloud-based
platforms, mobile health applications, or embedded diagnostic devices to
enable remote or point-of-care diagnosis of pneumonia .
7. The method of claim 4, wherein the diagnostic output is utilized to
automatically recommend treatment pathways based on the classification
result, thereby supporting personalized treatment decisions for bacterial
versus viral pneumonia cases.

5.CLAIMS
We claim that
I. An artificial intelligence-based system for detecting pneumonia, comprisin!
a Convolutional Neural Network (CNN) model trained on a plurality of labeled chest X
ray images, wherein the model classifies input images into categories comprising bacterial
pneumonia, viral pneumonia (including COVID-19), and nomnallungs.
2. The system of claim I, wherein the CNN model is trained using a dataset of at least 20,000
chest X-ray images with a unifomn resolution of224x224 pixels, enabling high-resolutior
feature extraction and consistent model input processing.
3. The system of claim I, wherein the model achieves a classification accuracy of at leas
95% on validation data through the aJPplication of optimization techniques including data
augmentation, dropout regularization, and learning rate scheduling .
4. A method for diagnosing pneumonia using chest X-ray images, comprising the steps of:
(a) receiving a digital chest X-ray image as input;
(b) preprocessing the image to a standardized resolution;
(c) feeding the image into a trained CNN model;
(d) outputting a diagnostic classification indicating presence and type of pneumonia or a
normal result.
5. The method of claim 4, further comprising integrating the trained model into a clinical
~ecision support system, enabling rea 1-time diagnostic assi~tljnceto medical practitioners.
6. The system of claim I, wherein the model is deployable on cloud-based platforms, mobile
health applications, or embedded diagnostic devices to e11able remote or point-of-can
diagnosis of pneumonia. . : :-:;
7. The method of claim 4, wherein the diagnostic outputJS. \lli!ized to automaticalh
recommend treatment pathways based on the classification result, thereby supporting
personalized treatment decisions for bacterial versus viral pneumonia cases