Microscopic blood cell classification using deep learning and image processing techniques.
This project implements a CNN to classify WBCs from microscopic images into 8 different classes. The model is trained using stratified 5-fold CV and a weighted random sampler. It supports single/batch inference. All modules conform to an interface file-based grading/evaluation system.
Multi-class blood cell classification using CNNs, transfer learning, and data augmentation, evaluated on Codabench.
Academic deep learning project comparing CNNs, Batch Normalization and ResNet18 transfer learning for blood cell image classification.
Deep learning-based blood cell segmentation and classification using U-Net architecture with automated RBC, WBC, and platelet counting. Achieved 92.83% validation accuracy.
Deep learning-based biomedical image classification using ResNet18 for automated peripheral blood cell recognition with Grad-CAM explainability.
A CNN classification model that classifies between blood cells
End-to-end medical computer vision pipeline for blood cell classification, anomaly detection, segmentation, and explainability.
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