Machine learning approach of automatic identification and counting of blood cells (RBC, WBC, and Platelet) with KNN and IOU based verification.

The complete blood count (CBC) dataset contains a total of 360 blood smear images of red blood cells (RBCs), white blood cells (WBCs), and Platelets with annotations.

DDxT: Deep Generative Transformer Models for Differential Diagnosis

Graph registration network using representative templates

Advanced deep learning framework for OCT retinal analysis with multi-architecture comparison for automated retinal layer segmentation, ERM detection, and vesicle analysis using state-of-the-art neural networks.

A simple, robust, multi-state Java implementation of the Naive Bayes classification algorithm.

DEFFA-UNet: PyTorch implementation for retinal vessel segmentation with dual encoding and attention mechanisms. Outperforms existing methods on DRIVE, CHASE_DB1, and STARE datasets for automated ophthalmology diagnosis.