Blood Cancer Detection from Peripheral Blood Smear Images

Using Deep Learning and Streamlit Interface

Abstract

This project implements an automated system for detecting and classifying blood cancer (lymphoma) from peripheral blood smear (PBS) images using deep learning techniques. The system provides a user-friendly web interface built with Streamlit, allowing medical professionals to upload PBS images and receive instant predictions about the presence and severity of lymphoma.

1. Introduction

1.1 Background

Blood cancer, particularly lymphoma, is a serious condition that requires early detection for effective treatment. Traditional manual microscopic examination of peripheral blood smears is time-consuming and subject to human error. This project aims to automate and enhance the detection process using artificial intelligence.

1.2 Objectives

• Develop an automated system for blood cancer detection from PBS images
• Create a user-friendly interface for medical professionals
• Provide quick and accurate predictions for early diagnosis
• Reduce the manual effort required in PBS analysis

2. Methodology

2.1 Data Collection and Preprocessing

• Collection of PBS image dataset
• Image preprocessing techniques applied:
• Normalization
• Resizing
• Augmentation (if applicable)

2.2 Model Architecture

• Description of the deep learning model used
• Network architecture details
• Training parameters and optimization techniques

2.3 System Implementation

• Technologies used:
• Python for model development
• Deep learning framework (TensorFlow/PyTorch)
• Streamlit for web interface
• System workflow:
• Image upload through web interface
• Image preprocessing
• Model prediction
• Result display

3. User Interface

The system features a Streamlit-based web interface with the following components:
- Image upload section
- Preview of uploaded image
- Prediction results display
- Confidence scores
- Visual indicators for severity levels

4. Results and Performance

4.1 Model Performance Metrics

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix

4.2 System Benefits

• Rapid detection capabilities
• Reduced manual examination time
• Consistent and objective analysis
• Easy-to-use interface
• Immediate results availability

5. Future Enhancements

• Integration with hospital management systems
• Mobile application development
• Enhanced visualization features
• Support for multiple types of blood cancers
• Real-time analysis capabilities

6. Conclusion

This project demonstrates the successful implementation of an automated blood cancer detection system using deep learning and modern web technologies. The system provides medical professionals with a powerful tool for quick and accurate lymphoma detection from PBS images.

Appendix

A. Technical Requirements

• Python 3.x
• Streamlit
• Deep Learning Framework
• Required Python packages:
  streamlit tensorflow/pytorch opencv-python numpy pillow

B. Installation and Usage Instructions

1. Clone the repository
2. Install required packages
3. Run the Streamlit application
4. Upload PBS image
5. View results