# 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:

  1. Image upload through web interface

  2. Image preprocessing

  3. Model prediction

  4. 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