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# Blood Cancer Detection from Peripheral Blood Smear Images





  
  

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## Using Deep Learning and Streamlit Interface





  
  

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### Abstract





  
  

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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.





  
  

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### 1. Introduction





  
  

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#### 1.1 Background





  
  

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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.





  
  

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#### 1.2 Objectives





  
  

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- Develop an automated system for blood cancer detection from PBS images





  
  

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- Create a user-friendly interface for medical professionals





  
  

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- Provide quick and accurate predictions for early diagnosis





  
  

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- Reduce the manual effort required in PBS analysis





  
  

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### 2. Methodology





  
  

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#### 2.1 Data Collection and Preprocessing





  
  

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- Collection of PBS image dataset





  
  

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- Image preprocessing techniques applied:





  
  

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  - Normalization





  
  

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  - Resizing





  
  

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  - Augmentation (if applicable)





  
  

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#### 2.2 Model Architecture





  
  

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- Description of the deep learning model used





  
  

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- Network architecture details





  
  

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- Training parameters and optimization techniques





  
  

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#### 2.3 System Implementation





  
  

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- Technologies used:





  
  

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  - Python for model development





  
  

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  - Deep learning framework (TensorFlow/PyTorch)





  
  

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  - Streamlit for web interface





  
  

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- System workflow:





  
  

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  1. Image upload through web interface





  
  

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  2. Image preprocessing





  
  

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  3. Model prediction





  
  

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  4. Result display





  
  

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### 3. User Interface





  
  

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The system features a Streamlit-based web interface with the following components:





  
  

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- Image upload section





  
  

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- Preview of uploaded image





  
  

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- Prediction results display





  
  

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- Confidence scores





  
  

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- Visual indicators for severity levels





  
  

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### 4. Results and Performance





  
  

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#### 4.1 Model Performance Metrics





  
  

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- Accuracy





  
  

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- Precision





  
  

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- Recall





  
  

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- F1-Score





  
  

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- Confusion Matrix





  
  

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#### 4.2 System Benefits





  
  

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- Rapid detection capabilities





  
  

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- Reduced manual examination time





  
  

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- Consistent and objective analysis





  
  

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- Easy-to-use interface





  
  

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- Immediate results availability





  
  

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### 5. Future Enhancements





  
  

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- Integration with hospital management systems





  
  

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- Mobile application development





  
  

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- Enhanced visualization features





  
  

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- Support for multiple types of blood cancers





  
  

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- Real-time analysis capabilities





  
  

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### 6. Conclusion





  
  

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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.





  
  

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### Appendix





  
  

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#### A. Technical Requirements





  
  

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- Python 3.x





  
  

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- Streamlit





  
  

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- Deep Learning Framework





  
  

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- Required Python packages:





  
  

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  ```





  
  

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  streamlit





  
  

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  tensorflow/pytorch





  
  

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  opencv-python





  
  

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  numpy





  
  

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  pillow





  
  

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  ```





  
  

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#### B. Installation and Usage Instructions





  
  

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1. Clone the repository





  
  

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2. Install required packages





  
  

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3. Run the Streamlit application





  
  

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4. Upload PBS image





  
  

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5. View results





  
  

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