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metadata
license: cc
dataset_info:
  features:
    - name: image
      dtype: image
    - name: annotation
      dtype: image
    - name: cancer_type
      dtype:
        class_label:
          names:
            '0': bening_breast_cancer
            '1': malignant_breast_cancer
  splits:
    - name: train
      num_bytes: 182212150
      num_examples: 547
  download_size: 181940841
  dataset_size: 182212150
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

breastcanc-ultrasound-class

Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

total_deaths

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

death_composition

Moreover, breast cancer has the highest share of number of cases/100 people worldwide (0.23 cases/100 people; IHME - Global Burden of Disease), as shown in table1 :

Table 1

Type of Cancer Cases per 100 people
Breast Cancer 0.23
Colon and Rectum Cancer 0.14
Prostate cancer 0.13
Bladder Cancer 0.034
Stomach Cancer 0.033

In this sense, it is more than vital to put intense effort into precision medicine and diagnostic tools for what concerns breast cancer: part of this effort should involve making curated dataset of diagnostic images available to the large public.

Dataset source and composition

This dataset is part of the one kindly provided by Walid Al-Dhabyani and collaborators in 2019, and encompasses 547 images, 387 representing benign breast cancer ultrasound images and 160 of them representing malignant breast cancer ultrasound images, with their related masks.

License, references and citation

The dataset is hereby provided under CC family licenses.

Please cite Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863 when using it.