This project leverages deep learning techniques to classify blood cell images into eight distinct types, including basophils, neutrophils, lymphocytes, and more. The final model achieves 93% accuracy on the test set, using EfficientNetV2S as a feature extractor for image classification. This project demonstrates the application of convolutional neural networks (CNNs) for medical image classification, showcasing the power of deep learning in the healthcare domain.
- 13,759 RGB images (96x96 pixels each)
- 8 cell type classifications, including basophils, neutrophils, lymphocytes, and others
The project is organized into multiple Jupyter notebooks, each serving a specific purpose:
- Model Implementation Notebooks: Separate notebooks for each tested deep learning architecture (e.g., EfficientNetV2S, CNN).
- Dataset Manipulation Notebook: A notebook detailing the preprocessing steps applied to the dataset, including data augmentation, normalization, and encoding of labels.