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+# 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
+