|
--- |
|
dataset_info: |
|
features: |
|
- name: image |
|
sequence: |
|
sequence: uint8 |
|
- name: head |
|
dtype: uint8 |
|
- name: vacuole |
|
dtype: uint8 |
|
- name: acrosome |
|
dtype: uint8 |
|
splits: |
|
- name: train |
|
num_bytes: 4359000 |
|
num_examples: 1000 |
|
- name: valid |
|
num_bytes: 1046160 |
|
num_examples: 240 |
|
- name: test |
|
num_bytes: 1307700 |
|
num_examples: 300 |
|
download_size: 4962520 |
|
dataset_size: 6712860 |
|
license: cc-by-sa-4.0 |
|
task_categories: |
|
- image-classification |
|
pretty_name: The Modified Human Sperm Morphology Analysis Dataset |
|
--- |
|
# MHSMA: The Modified Human Sperm Morphology Analysis Dataset |
|
|
|
The MHSMA dataset is a collection of human sperm images from 235 patients with male factor infertility. Each image is labeled by experts for normal or abnormal sperm acrosome, head, vacuole, and tail. |
|
|
|
# Source |
|
Make sure to visit the [Github page](https://github.com/soroushj/mhsma-dataset). |
|
``` |
|
@article{javadi2019novel, |
|
title={A novel deep learning method for automatic assessment of human sperm images}, |
|
author={Javadi, Soroush and Mirroshandel, Seyed Abolghasem}, |
|
journal={Computers in Biology and Medicine}, |
|
volume={109}, |
|
pages={182--194}, |
|
year={2019}, |
|
doi={10.1016/j.compbiomed.2019.04.030} |
|
} |
|
``` |
