The GitHub repository titled Malaria-Transmission-Blocking-Drug by manikrishna-m presents a data processing and visualization pipeline designed to support research in malaria transmission-blocking drug development.

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🔬 Project Overview

This repository offers a structured pipeline utilizing the PHIDDLI model to process and analyze malaria-related datasets. The pipeline is tailored for handling raw image data, converting it into a format suitable for training and evaluating machine learning models aimed at identifying and understanding malaria transmission stages.([GitHub][1])

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⚙️ Key Features

• Data Conversion: Transforms raw image data into standardized JPG format for consistency.
• Label Extraction: Employs YOLOv5 to generate bounding boxes, facilitating precise localization of malaria-infected cells within images.
• Cell Segmentation: Isolates individual cells from images, enabling detailed analysis of each cell's characteristics.
• Data Organization: Structures processed data into directories (data/images/, data/labels/, data/cells/) for efficient access and management.([GitHub][2])

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📁 Repository Structure

• scripts/: Contains Python scripts for data processing tasks.
• data/: Organized folders for raw data (src/), processed images (images/), labels (labels/), and segmented cells (cells/).
• requirements.txt: Lists Python dependencies required to run the scripts.
• dvc.yaml & dvc.lock: Configuration files for Data Version Control, ensuring reproducibility and versioning of datasets.

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🛠️ Installation & Usage

1. Clone the Repository:

   git clone https://github.com/manikrishna-m/Malaria-Transmission-Blocking-Drug.git
   cd Malaria-Transmission-Blocking-Drug

2. Install Dependencies:

   pip install -r requirements.txt

3. Prepare Data: Download the malaria dataset from BioImages and place it in the data/src/ directory.

4. Run Data Processing Scripts: Execute the following commands to process the data:

   python scripts/convert_raw.py --input-folder data/src/ --output-folder data/images/ --output-format jpg
   python scripts/extract_yolov5_bboxes.py --input-folder data/src/ --output-folder data/labels/
   python scripts/extract_cells.py --labels-folder data/labels/ --images-folder data/images/ --output-folder data/cells/

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📌 Notes

• Data Structure: Ensure the dataset is organized as specified to maintain compatibility with the pipeline.
• Model Integration: While the repository focuses on data preprocessing, the structured output facilitates integration with machine learning models for further analysis.([GitHub][3])

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