bccd

• Description:

BCCD Dataset is a small-scale dataset for blood cells detection.

Thanks the original data and annotations from cosmicad and akshaylamba. The original dataset is re-organized into VOC format. BCCD Dataset is under MIT licence.

Data preparation is important to use machine learning. In this project, the Faster R-CNN algorithm from keras-frcnn for Object Detection is used. From this dataset, nicolaschen1 developed two Python scripts to make preparation data (CSV file and images) for recognition of abnormalities in blood cells on medical images.

export.py: it creates the file "test.csv" with all data needed: filename, class_name, x1,y1,x2,y2. plot.py: it plots the boxes for each image and save it in a new directory.

Image Type : jpeg(JPEG) Width x Height : 640 x 480

• Additional Documentation: Explore on Papers With Code

• Homepage: https://github.com/Shenggan/BCCD_Dataset

• Source code: tfds.datasets.bccd.Builder

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 7.51 MiB

• Dataset size: 7.34 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	72
'train'	205
'validation'	87

• Feature structure:

FeaturesDict({
    'image': Image(shape=(480, 640, 3), dtype=uint8),
    'image/filename': Text(shape=(), dtype=string),
    'objects': Sequence({
        'bbox': BBoxFeature(shape=(4,), dtype=float32),
        'label': ClassLabel(shape=(), dtype=int64, num_classes=3),
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
image	Image	(480, 640, 3)	uint8
image/filename	Text		string
objects	Sequence
objects/bbox	BBoxFeature	(4,)	float32
objects/label	ClassLabel		int64

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@ONLINE {BCCD_Dataset,
    author = "Shenggan",
    title  = "BCCD Dataset",
    year   = "2017",
    url    = "https://github.com/Shenggan/BCCD_Dataset"
}