---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Ascaris lumbricoides
          '1': Capillaria philippinensis
          '2': Enterobius vermicularis
          '3': Fasciolopsis buski
          '4': Hookworm egg
          '5': Hymenolepis diminuta
          '6': Hymenolepis nana
          '7': Opisthorchis viverrine
          '8': Paragonimus spp
          '9': Taenia spp. egg
          '10': Trichuris trichiura
  splits:
  - name: train
    num_bytes: 9040769251
    num_examples: 11000
  - name: test
    num_bytes: 2453076590.8
    num_examples: 2200
  download_size: 11737493762
  dataset_size: 11493845841.8
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
- image-classification
- image-segmentation
- object-detection
language:
- ar
- en
tags:
- classification
- parasitic egg
pretty_name: parasitic-egg
size_categories:
- 1K<n<10K
---
# Dataset Card for Parasitic Egg Image Classification Dataset

<!-- Provide a quick summary of the dataset. -->

This dataset card aims to be a base template for the Parasitic Egg Image Classification Dataset. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

This dataset is designed for the classification of parasitic eggs from microscopic images. Parasitic infections are a major health concern, particularly in developing countries, where parasites are a significant cause of illness. The dataset includes images of 11 types of parasitic eggs, each category containing 1,000 images derived from faecal smear samples. The dataset is intended to support the development of automated methods for detecting and classifying parasitic eggs, which is crucial for improving diagnostic capacity in laboratories.

- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** Not applicable
- **License:** [More Information Needed]

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

The dataset is primarily intended for use in the development and testing of machine learning models aimed at automating the detection and classification of parasitic eggs in microscopic images. It can be utilized for both conventional statistical models and deep learning techniques.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

The dataset should not be used for applications beyond the detection and classification of parasitic eggs, such as diagnosing unrelated medical conditions or for any non-medical image classification tasks.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset consists of images grouped into 11 categories corresponding to different types of parasitic eggs. Each category includes 1,000 images, making it suitable for balanced classification tasks. 

- **category_id 0:** Ascaris lumbricoides
- **category_id 1:** Capillaria philippinensis
- **category_id 2:** Enterobius vermicularis
- **category_id 3:** Fasciolopsis buski
- **category_id 4:** Hookworm egg
- **category_id 5:** Hymenolepis diminuta
- **category_id 6:** Hymenolepis nana
- **category_id 7:** Opisthorchis viverrine
- **category_id 8:** Paragonimus spp
- **category_id 9:** Taenia spp. egg
- **category_id 10:** Trichuris trichiura

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

The dataset was created to address the need for automated diagnostic tools in the detection and classification of parasitic eggs, a critical task in combating parasitic infections in resource-limited settings.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

The images in the dataset are sourced from faecal smear samples, which are commonly used in the diagnosis of parasitic infections.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

The images were collected from laboratory samples, processed, and categorized into 11 distinct classes of parasitic eggs. Further details on the data selection criteria, filtering methods, and processing tools are needed.

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

The source data producers are experts in parasitology who collected and prepared the faecal smear samples for imaging.

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

The annotations were likely performed by trained parasitologists or lab technicians familiar with identifying parasitic eggs in microscopic images. More information is needed on the specifics of the annotation process.

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

The dataset does not contain personal, sensitive, or private information, as it consists of microscopic images of parasitic eggs, which are not tied to individual identities.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the potential biases in the dataset, such as the possible over-representation of certain parasitic egg types or variations in image quality. More information is needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[More Information Needed]

## More Information [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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