7th sem project_Report-format.pdf

A PROJECT REPORT
on
“HealthCure: 7 Disease Detection
Using Machine Learning”
Submitted to
KIIT Deemed to be University
In Partial Fulfillment of the Requirement for the Award of
BACHELOR’S DEGREE IN COMPUTER SCIENCE ENGINEERING
BY
Shubham Chaudhary 20051033
Shefali Mandal 20051756
Lakshmi Gupta 20051938
Deeksha Lakhotia 20051896
Sreeja Saha 20051040
Vanshvardhan Punia 20051048
UNDER THE GUIDANCE OF
Dr. Suchismita Rout
SCHOOL OF COMPUTER ENGINEERING
KALINGA INSTITUTE OF INDUSTRIAL TECHNOLOGY
BHUBANESWAR, ODISHA - 751024
December 2023

KIIT Deemed to be University
School of Computer Engineering
Bhubaneswar, ODISHA 751024
CERTIFICATE
This is certify that the project entitled
“HealthCure: 7 Disease Detection
Using Machine Learning“
submitted by
20051033 Shubham Chaudhary
20051756 Shefali Mandal
20051938 Lakshmi Gupta
20051896 Deeksha Lakhotia
20051040 Sreeja Saha
20051048 Vanshvardhan Punia
is a record of bonafide work carried out by them, in the partial
fulfillment of the requirement for the award of Degree of Bachelor of
Engineering (Computer Sci-ence & Engineering OR Information
Technology) at KIIT Deemed to be university, Bhubaneswar. This
work is done during the year 2022-2023, under our guidance.
Date: 12 / 05 / 2023
(Guide Name)
Dr. Suchismita Rout

Acknowledgements
We are profoundly grateful to Dr. Suchismita Rout for her expert
guidance and continuous encouragement throughout the entire
duration of this project. Her unwavering support has been
instrumental from the commencement to the completion of this
endeavor, ensuring that the project successfully achieves its intended
goals.
Shubham Chaudhary 20051033
Shefali Mandal 20051756
Lakshmi Gupta 20051938
Deeksha Lakhotia 20051896
Sreeja Saha 20051040
Vanshvardhan Punia 20051048

ABSTRACT
HealthCure is a revolutionary all-in-one medical solution that leverages the
power of artificial intelligence (AI) for the detection of seven major
diseases. This innovative platform integrates custom Convolutional Neural
Networks (CNNs), VGG-16, Random Forest, and XGBoost models for the
detection of Covid-19, brain tumors, breast cancer, Alzheimer's, diabetes,
pneumonia, and heart diseases. The project demonstrates significant
achievements in disease detection accuracy, with CNNs achieving a
remarkable 93% accuracy in Covid-19 detection.
The diverse methodologies employed for each disease category,
showcasing the adaptability of HealthCure. From the utilization of custom
CNN architectures for Covid-19 and Alzheimer's detection to the
application of VGG-16 for brain tumor detection, the project employs a
nuanced approach tailored to each medical condition. The use of Random
Forest for breast cancer and diabetes detection, as well as custom CNN
architectures for pneumonia, and XGBoost for heart disease detection,
underscores the versatility and effectiveness of the AI-driven HealthCure
platform.
Keywords: AI, Convolutional Neural Networks, Disease Detection,
HealthCure, Medical Diagnostics, Machine Learning, Artificial
Intelligence, CNN, VGG-16, Random Forest, XGBoost.

Contents
1 Introduction 1
2 / Literature Review 2
2.1 Sub Section Name........................... 2
3 Problem Statement / Requirement Specifications 3
3.1 Project Planning........................... 3
3.2 Project Analysis (SRS)................. 3
3.3 System Design ………………….. 3
3.3.1 Design Constraints …… 3
3.3.2 System Architecture (UML) / Block Diagram … 3
4 Implementation 4
4.1 Methodology / Proposal ........................... 4
4.2 Testing / Verification Plan ……………. 4
4.3 Result Analysis / Screenshots …………. 4
4.4 Quality Assurance …………………….. 4
5 Standard Adopted 5
5.1 Design Standards . . . . . . . . . . . . . . . 5
5.2 Coding Standards . . . . . . . . . . . . . . 5
5.3 Testing Standards . . . . . . . . . . . . . . . 5
6 Conclusion and Future Scope 6
6.1 Conclusion ……………………….. 6
6.2 Future Scope ………………………. 6
References 7
Individual Contribution 8
Plagiarism Report 9

Chapter 1
Introduction
In the era of rapid technological advancement, the integration of Artificial
Intelligence (AI) in healthcare has emerged as a beacon of hope, promising
innovative solutions to complex medical challenges. HealthCure, our
groundbreaking project, stands at the intersection of cutting-edge AI and
healthcare, presenting an all-encompassing medical solution focused on the
early detection of seven major diseases. Through the amalgamation of
sophisticated algorithms and advanced neural networks, HealthCure aims
to transform the landscape of disease diagnosis, bringing efficiency,
accuracy, and accessibility to the forefront of healthcare.
The overarching goal of HealthCure is to address the critical need for early
and accurate disease detection, laying the foundation for timely
intervention and improved patient outcomes. The project focuses on seven
pivotal health issues: Covid-19, Brain Tumour, Breast Cancer, Alzheimer's
Disease, Diabetes, Pneumonia, and Heart Disease. These diseases, with
varying degrees of prevalence and severity, collectively contribute to a
significant global health burden. By harnessing the power of AI,
HealthCure endeavors to provide a comprehensive and unified platform for
the detection and diagnosis of these ailments.
The utilization of Convolutional Neural Networks (CNNs) serves as the
backbone of HealthCure's disease detection capabilities. CNNs, inspired by
the intricate connectivity patterns of neurons in the human brain, excel in
image recognition tasks. The architecture of these networks allows them to
effectively capture spatial and temporal dependencies within medical
images, making them particularly well-suited for identifying intricate
patterns associated with various diseases. The reliance on CNNs
underscores our commitment to leveraging state-of-the-art technology to
enhance diagnostic accuracy.

The implementation of custom CNN architectures, alongside renowned
models like VGG-16, Random Forest, and XGBoost, underscores the
versatility of HealthCure. Each disease detection module is carefully
crafted to cater to the unique characteristics and features associated with
specific medical conditions. For instance, the Covid-19 detection module
employs a custom-made CNN architecture, achieving an impressive
accuracy rate of around 93%. Meanwhile, the Brain Tumour detection
module integrates VGG-16 for feature extraction, coupled with a custom
CNN for subsequent analysis, achieving an astonishing 100% accuracy in
initial tests.
Beyond the technical intricacies, HealthCure embodies a holistic approach
to healthcare. The project extends beyond mere disease detection, delving
into project planning, system design, and quality assurance. The emphasis
on design standards, coding standards, and testing protocols ensures the
robustness and reliability of the implemented AI models.
As we embark on this journey to revolutionize disease detection,
HealthCure not only represents a significant technological advancement but
also a testament to our commitment to the well-being of individuals
worldwide. The integration of AI in healthcare, as demonstrated by
HealthCure, has the potential to redefine medical practices, democratize
access to advanced diagnostics, and ultimately contribute to a healthier
global population.
In the following sections of this project report, we delve deeper into the
methodologies, results, and standards adopted in the implementation of
HealthCure, shedding light on the intricacies of our innovative approach.
The journey towards redefining disease detection has just begun, and
HealthCure stands at the forefront, paving the way for a future where AI
plays a pivotal role in shaping the landscape of healthcare.

Chapter 2
Basic Concepts/ Literature Review
In the pursuit of creating HealthCure, a comprehensive review of existing
literature and foundational concepts was conducted to inform the design
and implementation of the AI-powered disease detection system. The core
concept revolves around the integration of Convolutional Neural Networks
(CNNs) into healthcare, specifically for image-based disease diagnosis.
The literature review emphasizes the significance of CNNs in image
recognition tasks, drawing parallels between the connectivity patterns of
neurons in the human brain and the network architecture of CNNs. Existing
studies showcase the effectiveness of CNNs in capturing spatial and
temporal dependencies within medical images, providing a robust
foundation for disease detection.
Noteworthy contributions from research in the field include the successful
application of custom CNN architectures for Covid-19 detection, VGG-16
for brain tumor feature extraction, and the use of Random Forest and
XGBoost for various disease classifications. These studies provide insights
into the versatility of AI models and their adaptability to diverse medical
conditions.
Moreover, the literature review underscores the critical importance of early
disease detection in improving patient outcomes. Timely intervention based
on accurate diagnoses is highlighted as a key factor in mitigating the impact
of diseases such as breast cancer, Alzheimer's, diabetes, pneumonia, and
heart disease.
By building upon the findings and methodologies established in prior
research, HealthCure aims to contribute to the evolving landscape of AI in
healthcare, addressing the pressing need for precise and timely disease
detection. The review forms the conceptual foundation for the project,
guiding the implementation of innovative AI solutions for the benefit of
global healthcare.
1. Convolutional Neural Networks (CNNs) in Healthcare
This sub-topic explores the core principles of CNNs and their specific
application in healthcare. It starts by introducing the architecture of CNNs,
highlighting their convolutional layers, pooling layers, and fully connected

layers. The discussion emphasizes how CNNs are uniquely suited for
image recognition tasks due to their ability to automatically learn
hierarchical features and patterns.
2. Image Recognition for Disease Diagnosis
Building on the understanding of CNNs, this sub-topic delves into the
practical application of image recognition in disease diagnosis. It reviews
key studies and methodologies where medical images, such as X-rays, CT
scans, or MRI scans, were successfully used for disease detection. The
exploration covers how CNNs excel in identifying visual patterns
indicative of diseases, laying the groundwork for HealthCure's utilization of
AI for precise medical image analysis.
3. Previous Studies and Models
This sub-topic provides an overview of relevant studies and models in the
literature that align with the HealthCure project. It discusses the custom
CNN architectures used for Covid-19 detection, the application of VGG-16
for brain tumor feature extraction, and the utilization of Random Forest and
XGBoost for different disease classifications. This exploration allows for a
comparison of methodologies and informs the rationale behind the model
selection for each disease category in HealthCure.
4. Importance of Early Disease Detection
This subtopic emphasizes the critical role of early disease detection in
improving patient outcomes. It draws from studies that demonstrate the
impact of timely intervention on disease progression and patient survival
rates. By understanding the broader implications of early detection, the
HealthCure project gains a contextual framework that goes beyond
technical considerations, aligning with the ultimate goal of positively
influencing healthcare outcomes.
5. Integration of AI in Healthcare
Exploring broader trends, challenges, and successes in the integration of AI
in healthcare, this sub-topic provides a contextual understanding of the
larger landscape within which HealthCure operates. It touches on ethical
considerations, regulatory frameworks, and the transformative potential of
AI in improving healthcare delivery. This exploration helps position
HealthCure within the broader context of AI applications in the medical
field.

Chapter 3
Problem Statement / Requirement
Specifications
In contemporary healthcare, early disease detection is hindered by
conventional diagnostic methods, leading to delayed interventions and
compromised patient outcomes. Existing approaches often lack the
precision required for timely diagnosis. The HealthCure project addresses
this critical gap by harnessing the power of Artificial Intelligence,
specifically Convolutional Neural Networks (CNNs), to enhance disease
detection accuracy. The pressing need for a unified platform capable of
detecting seven major diseases, including Covid-19, brain tumors, and
diabetes, underscores the urgency of developing a comprehensive
AI-driven solution. HealthCure seeks to revolutionize healthcare by
providing a sophisticated, efficient, and accessible means of early disease
diagnosis.
3.1 Project Planning
Project Plan for HealthCure App
1. Project Initiation (Week 1-2)
Objective: Define the scope, goals, and objectives of HealthCure.
Tasks:
Conduct a project kickoff meeting.
Clearly define the seven disease detection modules.
Identify key stakeholders and their roles.
Establish project milestones.
2. Requirements Gathering and Analysis (Week 3-4)
Objective: Define the detailed requirements for each disease detection
module.

Tasks:
Collaborate with healthcare professionals to gather insights.
Identify specific data requirements for training AI models.
Document technical specifications for each module.
3. System Design (Week 5-8)
Objective: Create a robust system architecture for HealthCure.
Tasks:
Design custom CNN architectures for each disease detection module.
Develop UML diagrams and block diagrams for system visualization.
Define design constraints and guidelines.
4. Implementation (Week 9-16)
Objective: Implement AI models and develop the HealthCure app.
Tasks:
Develop and train CNN models for disease detection.
Integrate VGG-16, Random Forest, and XGBoost models as required.
Implement a user-friendly interface for the app.
5. Testing and Verification (Week 17-20)
Objective: Ensure the reliability and accuracy of disease detection.
Tasks:
Develop a comprehensive testing plan.
Conduct unit testing, integration testing, and system testing.
Address and rectify any issues identified during testing.
6. Quality Assurance (Week 21-22)
Objective: Ensure high-quality standards for HealthCure.
Tasks:
Implement coding standards and best practices.

Conduct code reviews and quality checks.
Validate the accuracy of disease detection results.
7. Documentation (Week 25-26)
Objective: Create comprehensive documentation for HealthCure.
Tasks:
Document the technical aspects of the AI models.
Create user manuals and guidelines for healthcare professionals.
Compile a project report detailing the development process.
System Design
3.3.1 Design Constraints
The design of HealthCure is subject to certain constraints that shape its
working environment, ensuring optimal performance and reliability.
Software Environment:
HealthCure is designed to operate in a Flask web application framework,
utilizing Python as the primary programming language.
Dependencies on external libraries include OpenCV, TensorFlow,
Scikit-learn, Werkzeug, and others. Compatibility with specific library
versions is crucial for consistent functionality.
Model Dependencies:
The accuracy and efficacy of disease detection heavily rely on pre-trained
models. Model updates and maintenance are essential to ensure alignment
with the latest medical knowledge.
Image Input Format:
HealthCure accepts image uploads in PNG, JPG, or JPEG formats.
Ensuring adherence to these formats is critical for accurate image
processing.
Hardware Considerations:
Efficient functioning of the AI models demands hardware capable of
handling image processing tasks. The hardware specifications must align
with the model requirements to avoid performance bottlenecks.
3.3.2 System Architecture (UML) / Block Diagram
The system architecture of HealthCure is structured to seamlessly integrate
various components for effective disease detection.
User Interface (UI):

The front end, built with HTML and Flask's template engine, provides an
intuitive and user-friendly interface for interacting with the application.
Backend Processing:
The Flask application handles user requests, manages file uploads, and
initiates disease detection processes.
Disease Detection Modules:
Each disease detection module (e.g., Covid, Brain Tumor, Breast Cancer)
integrates specific AI models (CNNs, Random Forest, etc.) for accurate
diagnoses.
Model Loading and Execution:
Pre-trained models are loaded into the system during startup. As user
requests are received, the relevant model is executed for disease prediction.
Result Rendering:
The system dynamically generates result pages, incorporating user
information and the outcome of disease detection.
Uploads and Storage:
Uploaded images are stored temporarily in the designated upload folder.
Managing file storage efficiently ensures smooth processing.
External Dependencies:
The system relies on external libraries, models, and frameworks.
Continuous monitoring and updates to these dependencies are crucial for
maintaining optimal performance.

Chapter 4
Implementation
The implementation phase of HealthCure involves translating the project
design into a functional and interactive web application. The codebase is
developed using Python and Flask, integrating various machine learning
models for disease detection. Key components include the user interface,
backend processing, and the integration of disease-specific models.
User Interface (UI):
HTML templates are used to create a user-friendly interface for each
disease detection module. Templates are designed to capture user input,
such as personal information and uploaded medical images.
Backend Processing:
Flask serves as the backend framework, handling HTTP requests and
orchestrating the entire disease detection process. The backend is
responsible for managing file uploads, invoking relevant models, and
dynamically rendering result pages.
Disease Detection Models:
Disease-specific models, including Convolutional Neural Networks
(CNNs), Random Forest, and others, are loaded during the startup phase.
These models are implemented using TensorFlow, Scikit-learn, and other
relevant libraries.
Methodology
The methodology employed in developing HealthCure revolves around a
systematic approach to ensure the accuracy, efficiency, and reliability of
disease detection. Key steps include:
Requirement Analysis:
Gathered requirements from healthcare professionals to define the scope of
disease detection modules and identified specific data requirements for
model training.
Model Selection and Development:

Selected appropriate machine learning models for each disease detection
module, considering factors like accuracy and computational efficiency.
Implemented models using established libraries and frameworks.
User Interface Design:
Developed an intuitive user interface using HTML and Flask templates,
ensuring a seamless and engaging user experience. Designed input forms
and result pages for each disease detection module.
Testing and Quality Assurance:
Conducted rigorous testing, including unit testing, integration testing, and
system testing, to identify and address any issues in the application.
Adhered to coding standards and best practices for quality assurance.
Testing
The testing phase is crucial for ensuring the functionality and reliability of
HealthCure. Key aspects of testing include:
Unit Testing:
Individual components, such as disease detection models and UI elements,
are tested in isolation to verify their correctness and functionality.
Integration Testing:
Testing the interaction between different components to ensure seamless
communication. This includes testing the coordination between the UI,
backend processing, and disease-specific models.
System Testing:
The entire system is tested to validate end-to-end functionality. This
involves simulating user interactions and assessing the system's response,
including model predictions and result rendering.
Result Analysis
The result analysis phase involves examining the output of the disease
detection models and evaluating the overall performance of HealthCure.
Key considerations include:
Accuracy Assessment:
Analyzing the accuracy achieved by each disease detection module based
on testing and validation data. This includes metrics such as precision,
recall, and F1 score.
User Experience Evaluation:

Gathering feedback from users to assess the application's usability and
responsiveness. Understanding user interactions and preferences
contributes to ongoing improvements.
Model Performance Metrics:
Evaluating the performance of machine learning models, considering
factors like sensitivity, specificity, and overall prediction reliability.
Continuous monitoring allows for model refinement.
Covid-19 Detection
● Used custom-made CNN architecture for this detection.
● The accuracy achieved was around 93%.
Brain Tumor detection

● Used VGG-16 for feature extraction.
● Used custom-made CNN ahead of CNN.
● The accuracy achieved was around 100%
Breast Cancer Detection
● Used Random Forest for this use case.
● The accuracy achieved was around 91.81%
Alzheimer Detection
● Trained CNN architecture for this use case.
● The accuracy achieved was around 73.54%.

Diabetes detection
● Used Random Forest for this use case.
Pneumonia Detection
● Used custom CNN architecture for this use case.

Heart Disease Detection
● Used XGBoost for this use case.

Chapter 5
5. Standards Adopted
5.1 Design Standards
In adherence to industry best practices, HealthCure follows established
design standards to ensure consistency, reliability, and maintainability of
the project. The design standards encompass:
UML Diagrams:
Unified Modeling Language (UML) is employed for system visualization.
Class diagrams, sequence diagrams, and other UML representations are
used to document and communicate the system architecture effectively.
Database Design Standards:
Standards for structuring and organizing the database schema are followed.
This includes normalization practices, defining relationships, and
optimizing queries for efficient data retrieval.
5.2 Coding Standards
To maintain code quality and readability, HealthCure adheres to a set of
coding standards. Some of the coding standards include:
Conciseness:
Write code with brevity, emphasizing clarity and simplicity. Concise code
is easier to understand and maintain.
Naming Conventions:
Utilize consistent and descriptive naming conventions for variables,
functions, and classes. This enhances code readability and comprehension.
Indentation:
Use indentation consistently to denote the structure of the code. Proper
indentation improves code organization and readability.
Modularization:
Break down code into modular functions, each addressing a specific task.
This practice enhances code reusability and maintainability.
Function Length:

Avoid lengthy functions; each function should ideally perform a single,
well-defined task. This promotes code modularity and ease of debugging.
5.3 Testing Standards
HealthCure adheres to industry-standard testing practices to ensure the
reliability and quality of the product. Relevant ISO and IEEE standards for
testing and verification include:
ISO 9001:
The ISO 9001 standard for quality management systems is followed to
ensure that HealthCure meets international quality standards.
IEEE 829:
IEEE 829 standard for Software Test Documentation is utilized for creating
comprehensive test plans, test cases, and test reports.
IEEE 610.12:
Adherence to the IEEE 610.12 standard ensures consistency in the
definitions of terms related to software engineering and testing.

Chapter 6
Conclusion:
HealthCure marks a milestone in the landscape of medical diagnostics,
harnessing the capabilities of artificial intelligence to redefine disease
detection. The amalgamation of various disease-specific models, including
Convolutional Neural Networks (CNNs), Random Forest, and XGBoost,
has yielded promising results, showcasing commendable accuracy rates
across an array of medical conditions. Rigorous testing and a commitment
to coding and design standards have solidified HealthCure's position as a
reliable and accessible tool for preliminary disease diagnosis.
The project's significance lies not only in its technical achievements but in
its potential to revolutionize healthcare. By providing a cost-effective and
efficient means of disease detection, HealthCure stands as a beacon of
innovation in the intersection of technology and healthcare expertise. The
user-friendly interface, crafted using HTML and Flask, ensures
accessibility for a diverse user base, emphasizing inclusivity in healthcare
solutions.
As HealthCure concludes its initial phase, the project stands as a testament
to the possibilities that arise when artificial intelligence meets medical
diagnostics. The collaboration between technology and medical knowledge
is emblematic of the future of diagnostic tools, ushering in a new era of
efficient and accurate health assessments.
Future Scope:
HealthCure's journey is far from complete, with a rich tapestry of
possibilities awaiting exploration in its future scope.
Enhanced Model Training:
Continuous refinement and retraining of disease detection models with
expanded datasets can further elevate accuracy, ensuring that HealthCure
remains at the forefront of medical knowledge.
Incorporation of New Diseases:
The modular framework of HealthCure allows for seamless integration of
new disease detection modules. This opens avenues for expanding the
range of covered diseases, accommodating emerging health concerns.
Real-Time Monitoring:

Introducing real-time monitoring features would empower healthcare
professionals with dynamic insights into disease trends. This could prove
invaluable for proactive public health management and early intervention
strategies.
Mobile Application Development:
Extending HealthCure to mobile platforms through application
development can significantly enhance accessibility. Users could
conveniently perform health assessments using their smartphones,
widening the reach of the application.
Collaboration with Healthcare Providers:
Establishing partnerships with healthcare providers can facilitate the
integration of HealthCure into clinical settings. This collaborative approach
ensures a more comprehensive approach to patient care, merging
technological advancements with established healthcare practices.
Implementation of Explainable AI:
Incorporating explainable AI techniques can enhance the interpretability of
HealthCure's disease predictions. This transparency ensures that both users
and healthcare professionals can comprehend and trust the rationale behind
each diagnosis.

References
https://github.com/sayakpaul/Breast-Cancer-Detection-using-Deep-Learning
http://www.ctan.org/tex-archive/macros/latex/contrib/supported/IEEEtran/
https://github.com/ShwetaTatiya/Image-Classification-using-CIFAR-10-dataset
https://github.com/yashrane/Dog-Breed-Identification
https://github.com/jsalbert/Music-Genre-Classification-with-Deep-Learning
https://github.com/nishagandhi/DrowsyDriverDetection
https://github.com/PrathamSolanki/gender-recognition-by-voice
https://github.com/anandpawara/Real_Time_Image_Animation

INDIVIDUAL CONTRIBUTION REPORT
PROJECT TITLE: HealthCure: 7 Disease Detection
Using Machine Learning
STUDENT NAME: Shubham Chaudhary
STUDENT ROLL NUMBER: 20051033
Abstract:
The HealthCure project aims to revolutionize medical diagnostics using artificial
intelligence. The objective is to integrate AI algorithms for the detection of major
diseases. The project envisions detecting Covid-19, brain tumors, breast cancer,
Alzheimer's, diabetes, pneumonia, and heart diseases under one unified
platform.
Individual Contribution and Findings:
As the lead developer, my primary role was to design and implement the custom
Convolutional Neural Network (CNN) architecture for Covid-19 detection. I
meticulously researched and implemented the necessary filters and layers to
achieve an accuracy of 93%. My planning involved extensive literature review,
model training, and iterative testing.
Technical Findings:
The challenge was in optimizing the CNN architecture for efficient Covid-19
detection. Fine-tuning hyperparameters and ensuring compatibility with diverse
image datasets required careful consideration. The experience enhanced my
skills in image processing and deep learning, contributing to the overall success
of the project.
Individual Contribution to Project Report Preparation:
I took charge of preparing the "Covid-19 Detection" section in the project report.
This involved documenting the CNN architecture, training methodologies, and
discussing the achieved accuracy. Additionally, I contributed to the overall
coherence and formatting of the report.
Individual Contribution for Project Presentation and Demonstration:
In the project presentation, I elucidated the intricacies of the custom CNN
architecture used for Covid-19 detection. I demonstrated the model's robustness
through live testing scenarios, showcasing its potential in real-world medical
diagnostics.
Full signature of the student:
Shubham Chaudhary
…………………………….

PROJECT TITLE: HealthCure - AI-Driven Medical Diagnostic Platform
STUDENT NAME: Lakshmi Gupta
Abstract:
HealthCure aims to bring together artificial intelligence and medical diagnostics
for the detection of major diseases. This includes Covid-19, brain tumors, breast
cancer, Alzheimer's, diabetes, pneumonia, and heart diseases, consolidating
them into a unified platform.
My role in the project involved implementing the VGG-16 architecture for
feature extraction in brain tumor detection. The meticulous planning included
data preprocessing, model training, and optimizing the architecture. The
accuracy achieved was an impressive 100% in initial tests on a small dataset.
Technical Findings:
Implementing VGG-16 for brain tumor detection required careful consideration
of layer configurations and model complexity. The experience enhanced my
understanding of deep learning architectures and their applications in medical
imaging.
I took responsibility for drafting the "Brain Tumor Detection" section in the
project report. This included detailing the VGG-16 architecture, explaining the
preprocessing steps, and presenting the achieved accuracy. Additionally, I
contributed to the literature review section.
During the project presentation, I explained the significance of VGG-16 in brain
tumor detection and demonstrated the model's accuracy. The live demonstration
showcased the model's potential in real-world medical scenarios.
Lakshmi Gupta
…………………………….

STUDENT NAME: Deeksha Lakhotia
STUDENT ROLL NUMBER : 20051896
Abstract:
HealthCure aspires to revolutionize medical diagnostics through AI, targeting
diseases like Covid-19, brain tumors, breast cancer, Alzheimer's, diabetes,
pneumonia, and heart diseases. The platform unifies these detections into one
comprehensive solution.
My primary responsibility was the implementation of the Random Forest
algorithm for breast cancer detection. Thorough planning involved data
preprocessing, model training, and hyperparameter tuning. The achieved
accuracy was approximately 91.81%, contributing significantly to the project's
success.
Technical Findings:
Implementing Random Forest for medical diagnostics required a nuanced
understanding of feature importance and ensemble learning. The experience
enhanced my expertise in machine learning and its applications in healthcare.
I took the lead in crafting the "Breast Cancer Detection" section of the project
report. This encompassed elucidating the Random Forest algorithm, presenting
the achieved accuracy, and contributing to the discussion on its implications in
medical diagnostics.
During the project presentation, I effectively communicated the significance of
Random Forest in breast cancer detection. The live demonstration showcased the
algorithm's robustness and potential in real-world medical scenarios.
Deeksha
…………………………….

STUDENT NAME: Shefali Mandal
Abstract:
HealthCure introduces an AI-driven approach to medical diagnostics, targeting
diseases such as Covid-19, brain tumors, breast cancer, Alzheimer's, diabetes,
pneumonia, and heart diseases. The platform integrates these detections into one
cohesive solution.
My primary role involved training the Convolutional Neural Network (CNN)
architecture for Alzheimer's detection. The planning encompassed data
preprocessing, model training, and fine-tuning. Despite the challenges, the
achieved accuracy was 73.54%, contributing significantly to the project's
diversity.
Technical Findings:
Training CNN for Alzheimer's detection demanded careful consideration of
spatial dependencies and feature extraction. The experience enhanced my
proficiency in deep learning techniques and their applications in neurological
diagnostics.
I contributed significantly to the "Alzheimer Detection" section in the project
report. This involved detailing the CNN architecture, explaining the training
process, and discussing the achieved accuracy. Additionally, I played a key role
in the literature review.
During the project presentation, I delved into the intricacies of CNN in
Alzheimer's detection, highlighting its potential applications. The live
demonstration showcased the model's capability in identifying
Alzheimer's-related patterns.
Shefali
…………………………….

STUDENT NAME: VANSHVARDHAN PUNIA
Abstract:
HealthCure aims to redefine medical diagnostics through the fusion of AI and
healthcare, detecting diseases like Covid-19, brain tumors, breast cancer,
Alzheimer's, diabetes, pneumonia, and heart diseases. The platform integrates
these detections into a cohesive solution.
My primary responsibility was the implementation of Random Forest for
diabetes detection. Planning involved comprehensive data preprocessing, model
training, and optimizing hyperparameters. Despite the inherent complexity, the
achieved accuracy was 66.8%, contributing to the project's holistic approach.
Technical Findings:
Implementing Random Forest for diabetes detection required a thorough
understanding of feature importance and model interpretability. The experience
enriched my knowledge of machine learning applications in healthcare.
I took the lead in developing the "Diabetes Detection" section of the project
report. This included detailing the Random Forest algorithm, presenting the
accuracy, and contributing to the discussions on its implications in diabetes
diagnosis.
During the project presentation, I elucidated the significance of Random Forest
in diabetes detection. The live demonstration showcased the algorithm's
effectiveness and potential in real-world medical scenarios.
Vanshvardhan
…………………………….

STUDENT NAME: SREEJA SAHA
Abstract:
HealthCure envisions the fusion of AI and healthcare for enhanced diagnostics,
targeting diseases like Covid-19, brain tumors, breast cancer, Alzheimer's,
diabetes, pneumonia, and heart diseases. The platform integrates these
detections into one comprehensive solution.
My primary role involved the design and implementation of a custom
Convolutional Neural Network (CNN) architecture for pneumonia detection.
Planning encompassed data preprocessing, model training,and fine-tuning.
Despite challenges, the achieved accuracy was approximately 83.17%,
contributing significantly to the project's respiratory health component.
Technical Findings:
Designing a custom CNN architecture for pneumonia detection demanded an
understanding of image processing nuances and spatial dependencies. The
experience bolstered my proficiency in deep learning techniques, particularly in
respiratory diagnostics.
I took charge of crafting the "Pneumonia Detection" section in the project report.
This involved detailing the custom CNN architecture, explaining the
preprocessing steps, and presenting the achieved accuracy. Additionally, I
contributed to the project's methodology section.
During the project presentation, I elucidated the intricacies of the custom CNN
architecture in pneumonia detection. The live demonstration showcased the
model's robustness and its potential application in identifying pneumonia-related
patterns.
Sreeja
…………………………….

TURNITIN PLAGIARISM REPORT
(This report is mandatory for all the projects and plagiarism
must be below 25%)

7th sem project_Report-format.pdf

7th sem project_Report-format.pdf

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