Early and accurate identification of lung abnormalities such as pneumonia, tuberculosis, and lung cancer is critical for timely medical intervention and improved patient outcomes. This paper proposes a fast and efficient framework that leverages Explainable Artificial Intelligence (XAI) to analyze chest CT scans and X-ray images for automated lung abnormality detection. The framework integrates state-of-the-art deep learning models with explainability techniques like Grad-CAM and LIME to ensure diagnostic transparency and clinician trust. Emphasis is placed on computational efficiency to enable real-time or near-real-time diagnostics, making the system suitable for deployment in both advanced and resource-limited healthcare settings. Extensive experimentation on publicly available datasets demonstrates the proposed system’s high accuracy, sensitivity, and interpretability in identifying a wide range of lung conditions.

1. RAJEEV INSTITUTE OF TECHNOLOGY
HASSAN
DEPARTMENT OF COMPUTER SCIENCE AND
ENGINEERING
PROJECT ON
UNDER THE GUIDENCE OF,
Mr. CHIRANJEEVI M R
ASSISTANT PROFESSOR,
DEPT OF CSE,
RIT HASSAN.
SUBMITTED BY,
SHAIK MOHAMMED AYAN [4RA21CS077]
SURAJ B [4RA22CS413]
KIRAN KUMAR C V [4RA22CS405]
GAGAN B G [4RA22CS403]
“Lung Abnormality Identification
With Explainable AI”

2. CONTENT
1.Abstract
2.Introduction
3.Objective
4.CNN (Convolutional Neural Network)
5.Problem Statement
6.Block Diagram
7.Methodology
8.Implemented Results (from mini project)
9.Work to be completed (main project)
10.Conclusion
11.References

3. ABSTRACT
Early and accurate identification of lung abnormalities such as pneumonia, tuberculosis, and lung
cancer is critical for timely medical intervention and improved patient outcomes. This paper proposes a fast
and efficient framework that leverages Explainable Artificial Intelligence (XAI) to analyze chest CT scans
and X-ray images for automated lung abnormality detection. The framework integrates state-of-the-art deep
learning models with explainability techniques like Grad-CAM and LIME to ensure diagnostic transparency
and clinician trust. Emphasis is placed on computational efficiency to enable real-time or near-real-time
diagnostics, making the system suitable for deployment in both advanced and resource-limited healthcare
settings. Extensive experimentation on publicly available datasets demonstrates the proposed system’s high
accuracy, sensitivity, and interpretability in identifying a wide range of lung conditions.

4. INTRODUCTION
• Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early and accurate diagnosis can
significantly improve patient outcomes.
• Traditionally, detection involves radiologists analyzing X-ray images, a time-consuming and subjective process.
• To overcome these challenges, this project proposes an AI-based system for automated detection of lung cancer
using chest X-ray images.
• By leveraging deep learning techniques, the model aims to classify X-ray images as either cancerous or non-
cancerous, enabling faster and more accurate diagnoses in clinical settings.

5. OBJECTIVE
1. To develop a robust deep learning-based framework for the automatic detection of lung abnormalities
using chest CT and X-ray images.
2. To integrate Explainable AI techniques that enhance model transparency and provide visual explanations
for the detected abnormalities.
3. To optimize the system for speed and efficiency, enabling real-time or near-real-time performance
suitable for clinical applications.
4. To evaluate the model’s performance on benchmark datasets using key metrics like accuracy, sensitivity,
specificity, and inference time.
5. To ensure adaptability and scalability of the framework across diverse medical imaging modalities and
healthcare infrastructures.

6. CONVOLUTIONAL NEURAL NETWORK
1. Convolutional Layer: Finds patterns like edges or colors in the image.
2. ReLU Layer: Removes unwanted parts (like background noise) by keeping
only important values.
3. Pooling Layer: Makes the image smaller by picking the most important
information.
4. Fully Connected Layer: Combines all the information to make a final
decision (like what the image shows)

7. PROBLEM STATEMENT
Input:
 Chest X-ray images (in formats like PNG, JPG)
 Dataset: (e.g., Kaggle’s Lung Cancer or Chest X-ray datasets)
Process:
 Preprocessing of images: resizing, denoising, normalization
 Feature extraction using Convolutional Neural Networks (CNN)
 Classification using deep learning (CNN-based model)
 Evaluation of performance using metrics (accuracy, precision, recall)
Output:
 Predicted label: Cancer Detected / Normal
 Confidence score of prediction

8. BLOCK DIAGRAM

9. METHODOLOGY
 Dataset Collection: Gathered chest X-ray images from publicly available datasets like Kaggle.
 Data Preprocessing:
o Image resizing to 224x224
o Normalization (pixel values between 0 and 1)
o Augmentation (rotation, flipping)
 Model Architecture:
o Used a custom CNN model with 3 convolutional layers
o Activation: ReLU, Pooling: MaxPooling
o Fully Connected Dense Layers for final classification
 Training:
o Split: 80% training, 20% testing
o Loss Function: Binary Crossentropy
 Evaluation:
o Confusion Matrix

10. IMPLEMENTED RESULT
 Dataset Used: 1,000+ X-ray images
 Accuracy Achieved: ~96% on test data
 Tools/Tech Stack: Python, Keras, TensorFlow, OpenCV,IDLE
 Sample Outputs:
 Input Image → Prediction: Cancer Detected
 Input Image → Prediction: Normal

11. WORK TO BE COMPLETED
 Increase Dataset Size: Integrate additional X-ray datasets
 Model Enhancement:
o Use transfer learning (VGG16, ResNet)
o Fine-tune hyperparameters
 GUI Development: Build an interface for doctors to upload X-rays and view results
 Deployment:
o Host the model using Flask
o Test on real-world data
 Evaluation with real-time hospital data

12. CONCLUSION
This study presents a comprehensive, explainable AI-based framework for the fast and
accurate identification of lung abnormalities using chest CT scan and X-ray images. By
integrating advanced deep learning models with explainability techniques such as Grad-CAM
and LIME, the proposed system not only achieves high diagnostic accuracy but also provides
critical insights into the model’s decision-making process, thereby enhancing clinical trust and
interpretability. The framework demonstrates strong performance across multiple datasets and
proves to be computationally efficient, making it viable for deployment in real-time clinical
environments, including resource-constrained settings. This work paves the way for more
transparent, scalable, and AI-assisted diagnostic tools that can significantly improve the early
detection and management of lung diseases.

13. REFERENCES
1. Kaggle Chest X-ray Dataset
2. S. Sharma, et al. “Deep Learning for Lung Cancer Detection,” IEEE Access, 2022.
3. Chollet, F. “Deep Learning with Python,” Manning, 2017.
4. www.tensorflow.org
5. www.opencv.org

14. THANK YOU