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Lung Cancer Using CNN

The document presents a paper on lung cancer detection using Convolutional Neural Networks (CNNs), highlighting the importance of early detection and the limitations of traditional methods. It outlines the methodology, including data preparation, image processing, model development, and evaluation, demonstrating the effectiveness of CNNs in improving diagnosis accuracy. Future work aims to address challenges like data imbalance and explore advanced techniques for better model performance.

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Suhani Kasare
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6 views10 pages

Lung Cancer Using CNN

The document presents a paper on lung cancer detection using Convolutional Neural Networks (CNNs), highlighting the importance of early detection and the limitations of traditional methods. It outlines the methodology, including data preparation, image processing, model development, and evaluation, demonstrating the effectiveness of CNNs in improving diagnosis accuracy. Future work aims to address challenges like data imbalance and explore advanced techniques for better model performance.

Uploaded by

Suhani Kasare
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Pimpri Chinchwad Education Trust’s

Pimpri Chinchwad College of Engineering, Pune

Paper Presentation Conference


AIML 2024-25 semester 2
Presentation By:

Lung Cancer Detection Using CNN


122B1E074 - Sarthak Jathar
122B1E075- Abhishek Jaybhaye
122B1E078- Vedant Joshi
122B1E079- Sandesh Kadelwar
122B1E081- Suhani Kasare
Guided By: Dr. Rajani P.K
Pimpri Chinchwad College of Engineering, Pune
1
Outline
• Introduction
• Problem Statement
• Objectives
• Literature Review
• Methodology
• Results
• Conclusion
• References

2
Introduction
The Importance of Early Detection

Lung cancer is a leading cause of cancer-related deaths, with 1.8 million


deaths annually. Early detection improves survival rates, but traditional
methods are time-consuming and error-prone. This research explores
CNNs for automated and accurate lung cancer detection using CT scans.

3
Problem Statement:

4
Objectives:

5
Literature Review:
CNNs in Medical Imaging

6
Methodology:
CNN Architecture and Training

Data Preparation:
Import libraries, load datasets, and visualize data
distribution.

Image Processing:
Resize, normalize, augment images, and split into
training/validation sets.
Model Development:

Build and train a CNN with convolutional layers, max


pooling, and fully connected layers.
Model Evaluation:

Assess performance using accuracy/loss


visualization, classification report, and confusion
matrix.
7
Results:
Model Performance

8
Conclusion:
CNN Effectiveness and Future Scope
● CNNs are highly effective for lung cancer detection from CT scans.
● The model achieved high accuracy, aiding early diagnosis and improving patient
outcomes.
● Challenges include data imbalance, overfitting, and dependency on high-quality
datasets.
Future Scope:
● Improve generalization with larger datasets.
● Explore transfer learning using models like VGG16/ResNet.
● Enhance data augmentation techniques for better robustness.

9
THANK YOU

10

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