Omkar2118/datasets huggingface dataset:
LymphoMNIST
Full name: LymphoMNIST

LymphoMNIST is a comprehensive dataset designed for the nuanced classification of lymphocyte images. It encompasses approximately 80,000 high-resolution 64x64 images, meticulously categorized into three primary classes: B cells, T4 cells, and T8 cells.​

Dataset Characteristics:

Size: ~80,000 images​
Resolution: 64x64 pixels​
Classes: B cells, T4 cells, T8 cells​
Format: MNIST-like standardized biomedical imagery​
Modality: Microscopy-based high-resolution cell images​

Motivation and Summary: LymphoMNIST aims to bridge the gap in biomedical image analysis by providing a dataset that is vast in scale and rich in detail. It supports a wide array of research endeavors, from fundamental biological studies to advanced computational model development.​

Potential Use Cases:

Medical Research: Studying lymphocyte morphology and characteristics​
Machine Learning & AI: Developing and evaluating image classification models​
AutoML & Benchmarking: Serving as a benchmark dataset for automated model training and performance evaluation​
Educational Purposes: Teaching deep learning concepts in biomedical imaging​
Data Collection Process: The dataset comprises high-resolution images of lymphocytes obtained through microscopy. Each image is standardized to a 64x64 pixel resolution to maintain consistency and facilitate analysis.​

Annotations and Labels: Each image is labeled as one of the three lymphocyte classes: B cells, T4 cells, or T8 cells. The labeling process was conducted by experts in the field to ensure accuracy.

Introducing paper: Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning