Data Science Intern - Data Labeling for Malaria Cell Classification
ImageClassification
Developed a convolutional neural network (CNN) model to classify cell images as malaria-infected or healthy. Labeled and curated image data to train and evaluate the model, ensuring high-quality input for AI training. Focused on optimizing data preprocessing for improved model accuracy and reliability.
• Achieved 96% accuracy on the classification task.
• Curated and labeled dataset of microscopy images for training and testing.
• Emphasized the importance of minimizing false negatives for early intervention.
• Utilized standard image annotation and labeling workflows within data science platforms.
Developed a convolutional neural network (CNN) model to classify cell images as malaria-infected or healthy. Labeled and curated image data to train and evaluate the model, ensuring high-quality input for AI training. Focused on optimizing data preprocessing for improved model accuracy and reliability. • Achieved 96% accuracy on the classification task. • Curated and labeled dataset of microscopy images for training and testing. • Emphasized the importance of minimizing false negatives for early intervention. • Utilized standard image annotation and labeling workflows within data science platforms.
2024 - 2024