All Projects → MahmudulAlam → Complete-Blood-Cell-Count-Dataset

MahmudulAlam / Complete-Blood-Cell-Count-Dataset

Licence: MIT License
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.

Projects that are alternatives of or similar to Complete-Blood-Cell-Count-Dataset

sqair
Implementation of Sequential Attend, Infer, Repeat (SQAIR)
Stars: ✭ 96 (+209.68%)
Mutual labels:  detection
Errata1337
No description or website provided.
Stars: ✭ 45 (+45.16%)
Mutual labels:  detection
AU R-CNN
The official implementation code of paper: "AU R-CNN:Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection".
Stars: ✭ 65 (+109.68%)
Mutual labels:  detection
CornerNet-Lite-Pytorch
🚨🚨🚨 CornerNet:基于虚拟仿真环境下的自动驾驶交通标志识别
Stars: ✭ 34 (+9.68%)
Mutual labels:  detection
Kaio-machine-learning-human-face-detection
Machine Learning project a case study focused on the interaction with digital characters, using a character called "Kaio", which, based on the automatic detection of facial expressions and classification of emotions, interacts with humans by classifying emotions and imitating expressions
Stars: ✭ 18 (-41.94%)
Mutual labels:  detection
connection checker
Android library for checking the internet connectivity of a device.
Stars: ✭ 26 (-16.13%)
Mutual labels:  detection
ManTraNet-pytorch
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch
Stars: ✭ 47 (+51.61%)
Mutual labels:  detection
AES
AES for microcontrollers (Arduino & Raspberry pi)
Stars: ✭ 116 (+274.19%)
Mutual labels:  cbc
Python-File-Encryptor
Encrypt and Decrypt files using Python (AES CBC MODE)
Stars: ✭ 51 (+64.52%)
Mutual labels:  cbc
AABO
Implementation for ECCV 2020 paper: AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling.
Stars: ✭ 24 (-22.58%)
Mutual labels:  detection
mri-deep-learning-tools
Resurces for MRI images processing and deep learning in 3D
Stars: ✭ 56 (+80.65%)
Mutual labels:  detection
TFLite-Mobile-Generic-Object-Localizer
Python TFLite scripts for detecting objects of any class in an image without knowing their label.
Stars: ✭ 42 (+35.48%)
Mutual labels:  detection
PlayerDetection
Player detection and ball detection in football matches using image processing(opencv).
Stars: ✭ 50 (+61.29%)
Mutual labels:  detection
keras cv attention models
Keras/Tensorflow attention models including beit,botnet,CMT,CoaT,CoAtNet,convnext,cotnet,davit,efficientdet,efficientnet,fbnet,gmlp,halonet,lcnet,levit,mlp-mixer,mobilevit,nfnets,regnet,resmlp,resnest,resnext,resnetd,swin,tinynet,uniformer,volo,wavemlp,yolor,yolox
Stars: ✭ 159 (+412.9%)
Mutual labels:  detection
apooxml
Generate YARA rules for OOXML documents.
Stars: ✭ 34 (+9.68%)
Mutual labels:  detection
person-detection
TensorRT person tracking RFBNet300
Stars: ✭ 30 (-3.23%)
Mutual labels:  detection
yolo3 tensorflow
yolo3 implement by tensorflow, including mobilenet_v1, mobilenet_v2
Stars: ✭ 48 (+54.84%)
Mutual labels:  detection
AOPG
Anchor-free Oriented Proposal Generator for Object Detection
Stars: ✭ 36 (+16.13%)
Mutual labels:  detection
Faster-RCNN-TensorFlow
TensorFlow implementation of Faster RCNN for Object Detection
Stars: ✭ 13 (-58.06%)
Mutual labels:  detection
YoloV5 JDE TensorRT for Track
A multi object tracking Library Based on tensorrt
Stars: ✭ 39 (+25.81%)
Mutual labels:  detection

Complete Blood Count (CBC) Dataset

GitHub stars GitHub forks GitHub issues GitHub license

The complete blood count (CBC) dataset contains 360 blood smear images along with their annotation files splitting into Training, Testing, and Validation sets. The training folder contains 300 images with annotations. The testing and validation folder both contain 60 images with annotations. We have done some modification over the original dataset to prepare this CBC dataset where some of the image annotation files contain very low red blood cells (RBCs) than actual and one annotation file does not include any RBC at all although the cell smear image contains RBCs. So, we clear up all the fallacious files and split the dataset into three parts. Among the 360 smear images, 300 blood cell images with annotations are used as the training set first, and then the rest of the 60 images with annotations are used as the testing set. Due to the shortage of the data, a subset of the training set is used to prepare the validation set which contains 60 images with annotations.

Download

Paper

Paper Paper

The dataset is modified and prepared for this paper for automatic identification and counting of blood cells🔗 If you use this dataset, please cite this paper:

Machine learning approach of automatic identification and counting of blood cells

@article{alam2019machine,
  title={Machine learning approach of automatic identification and counting of blood cells},
  author={Alam, Mohammad Mahmudul and Islam, Mohammad Tariqul},
  journal={Healthcare Technology Letters},
  volume={6},
  number={4},
  pages={103--108},
  year={2019},
  publisher={IET}
}

Data Description

Image

Each image is resized to 640 x 480 resolution.

N.B. Rectangular bounding boxes are converted to circular bounding boxes for representation.

Annotation Format

 <annotation>
	<folder>JPEGImages</folder>
	<filename>BloodImage_00395.jpg</filename>
	<path>/home/pi/detection_dataset/JPEGImages/BloodImage_00395.jpg</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>640</width>
		<height>480</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>RBC</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>25</xmin>
			<ymin>90</ymin>
			<xmax>127</xmax>
			<ymax>209</ymax>
		</bndbox>
	</object>
  . . . . . . . . 
  . . . . . . . . Rest of the RBC
  . . . . . . . . 
 	<object>
		<name>WBC</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>114</xmin>
			<ymin>66</ymin>
			<xmax>351</xmax>
			<ymax>294</ymax>
		</bndbox>
	</object>
	<object>
		<name>Platelets</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>472</xmin>
			<ymin>201</ymin>
			<xmax>540</xmax>
			<ymax>268</ymax>
		</bndbox>
	</object>
  . . . . . . . . 
  . . . . . . . . Rest of the platelets
  . . . . . . . . 
  </annotation>
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].