Datasets:

Angelou0516
/

BUSI

class_label stringclasses 3 values	image_id stringlengths 10 15	has_multifocal_lesion bool 2 classes
benign	benign (1)	false
benign	benign (10)	false
benign	benign (100)	true
benign	benign (101)	false
benign	benign (102)	false
benign	benign (103)	false
benign	benign (104)	false
benign	benign (105)	false
benign	benign (106)	false
benign	benign (107)	false
benign	benign (108)	false
benign	benign (109)	false
benign	benign (11)	false
benign	benign (110)	false
benign	benign (111)	false
benign	benign (112)	false
benign	benign (113)	false
benign	benign (114)	false
benign	benign (115)	false
benign	benign (116)	false
benign	benign (117)	false
benign	benign (118)	false
benign	benign (119)	false
benign	benign (12)	false
benign	benign (120)	false
benign	benign (121)	false
benign	benign (122)	false
benign	benign (123)	false
benign	benign (124)	false
benign	benign (125)	false
benign	benign (126)	false
benign	benign (127)	false
benign	benign (128)	false
benign	benign (129)	false
benign	benign (13)	false
benign	benign (130)	false
benign	benign (131)	false
benign	benign (132)	false
benign	benign (133)	false
benign	benign (134)	false
benign	benign (135)	false
benign	benign (136)	false
benign	benign (137)	false
benign	benign (138)	false
benign	benign (139)	false
benign	benign (14)	false
benign	benign (140)	false
benign	benign (141)	false
benign	benign (142)	false
benign	benign (143)	false
benign	benign (144)	false
benign	benign (145)	false
benign	benign (146)	false
benign	benign (147)	false
benign	benign (148)	false
benign	benign (149)	false
benign	benign (15)	false
benign	benign (150)	false
benign	benign (151)	false
benign	benign (152)	false
benign	benign (153)	false
benign	benign (154)	false
benign	benign (155)	false
benign	benign (156)	false
benign	benign (157)	false
benign	benign (158)	false
benign	benign (159)	false
benign	benign (16)	false
benign	benign (160)	false
benign	benign (161)	false
benign	benign (162)	false
benign	benign (163)	true
benign	benign (164)	false
benign	benign (165)	false
benign	benign (166)	false
benign	benign (167)	false
benign	benign (168)	false
benign	benign (169)	false
benign	benign (17)	false
benign	benign (170)	false
benign	benign (171)	false
benign	benign (172)	false
benign	benign (173)	true
benign	benign (174)	false
benign	benign (175)	false
benign	benign (176)	false
benign	benign (177)	false
benign	benign (178)	false
benign	benign (179)	false
benign	benign (18)	false
benign	benign (180)	false
benign	benign (181)	true
benign	benign (182)	false
benign	benign (183)	false
benign	benign (184)	false
benign	benign (185)	false
benign	benign (186)	false
benign	benign (187)	false
benign	benign (188)	false
benign	benign (189)	false

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BUSI — Breast Ultrasound Images Dataset

Re-hosted mirror of the Breast Ultrasound Images Dataset (Al-Dhabyani et al., 2020), collected at Baheya Hospital, Cairo, Egypt and originally distributed via the Cairo University Scholar page of co-author Aly Fahmy.

This mirror is intended for use with EasyMedSeg and provides:

A single canonical source (the original Cairo Univ. URL has unstable TLS).
Parquet schema with one row per image and a single binary mask.
For images with multifocal lesions, the multiple _mask_N.png files in the original release are merged into one binary mask via per-pixel logical OR (the convention adopted by the paper and by BUS-Set / Thomas et al. 2023).

Composition

Class	Images	Multifocal cases
benign	437	16
malignant	210	1
normal	133	0
Total	780	17

Image dimensions are variable (~190–719 px, average ~500×500 px). Source release is from 2018; 600 female patients, ages 25–75; GE LOGIQ E9 / E9 Agile B-mode.

Splits

BUSI has no official train/val/test split. This release ships a single train split. Downstream code is expected to define its own splits (BUS-Set provides reproducible ones).

Schema

Column	Type	Description
`image`	`Image`	Source PNG (RGB)
`mask`	`Image`	Binary mask (`L` mode, 0/255) — OR-merged across all mask files
`class_label`	`string`	`"benign"` / `"malignant"` / `"normal"`
`image_id`	`string`	e.g., `"benign (100)"`
`has_multifocal_lesion`	`bool`	`True` if the source had >1 mask file

Known data-quality caveats

Per Pawłowska et al. (2023, Data in Brief, PMC10293973):

~~235 duplicated images (~~19%); 8 lesions appear in both benign and malignant folders (label leakage).
~70 axilla (non-breast) images mis-categorized as breast.
295 images with overlaid annotations (calipers, doppler markers, text) intersecting the lesion ROI.
≥7 images contain a visible biopsy needle.

This mirror does not modify the source files (other than mask merging) — the above issues are inherited as-is. Cite Pawłowska et al. when reporting cleanups.

License

The original authors do not state an explicit license on the Cairo Univ. page or in the Data in Brief paper. Treat as research use, with required citation. Kaggle uploaders have tagged copies as CC BY 4.0 (uploader-asserted, not author-confirmed). Do not assume redistribution rights beyond research.

Citation

@article{Aldhabyani2020BUSI,
  title   = {Dataset of breast ultrasound images},
  author  = {Al-Dhabyani, Walid and Gomaa, Mohammed and Khaled, Hussein and Fahmy, Aly},
  journal = {Data in Brief},
  volume  = {28},
  pages   = {104863},
  year    = {2020},
  doi     = {10.1016/j.dib.2019.104863}
}

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