metadata

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

This dataset card aims to be a base template for the Parasitic Egg Image Classification Dataset. It has been generated using this raw template.

Dataset Details

Dataset Description

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]
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Language(s) (NLP): Not applicable
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Dataset Sources [optional]

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Uses

Direct Use

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

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

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

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

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

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?

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

Annotations [optional]

Annotation process

[More Information Needed]

Who are the annotators?

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

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

[More Information Needed]

Recommendations

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]

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Dataset Card Authors [optional]

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Dataset Card Contact

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