What is the Skin Cancer HAM10000 dataset?

The HAM10000 dataset is a large collection of dermatoscopic images used for skin lesion classification and cancer detection, ideal for AI and ML model training in dermatology.

How can this dataset be used in AI and machine learning?

This dataset helps train and validate AI models for automated diagnosis, lesion segmentation, and classification tasks in healthcare and telemedicine applications.

Is the dataset diverse and representative?

Yes, the dataset includes a diverse range of patients across age groups, skin tones, and global regions, providing balanced data for robust AI model performance.

How does GTS ensure data quality and compliance?

All datasets undergo multi-level quality checks and manual verification. GTS complies with ISO 9001:2015, ISO 27001:2013, GDPR, and HIPAA standards for secure and ethical data handling.

Can GTS provide custom healthcare datasets?

Yes. GTS offers custom medical imaging and healthcare datasets with scalable volume, multilingual support, and expert human annotation tailored to AI/ML research requirements.

Skin cancer: HAM10000

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Skin cancer: HAM10000

Datasets

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Skin cancer: HAM10000

Use Case

Dermatology Research

Description

The HAM10000 dataset is a publicly available dataset used in the field of case and machine learning for research related to the of skin lesions, particularly for the disclosure of skin cancer. "HAM" stands for "Human Against Machine," the goal of better automatic case diagnoses through machine learning.

About Dataset

The original HAM10000 dataset cannot be easily load in jupyter binder from the source.

Cases include a representative collection of all important symbolic categories in the realm of dusky lesions:

Actinic keratoses and intra cellular diseases/ Bowen’s disease (AKIEC),

basal cell carcinoma (BCC),

benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, BKL),

dermatofibroma (DF),

melanoma (MEL),

melanocytic nevi (NV)

vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, VASC).

Acknowledgments

Tschandl, P., Rinner, C., Apalla, Z. et al. Human–computer collaboration for skin cancer recognition. Nat Med (2020). https://doi.org/10.1038/s41591-020-0942-0

Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5, 180161 (2018). https://doi.org/10.1038/sdata.2018.161

Conclusion

Globose Technology Solution is dedicated to Combative Husk diseases research using the dataset. Our technology aids in early disclosure, improving patient outcomes, and reducing the impact of this disease. We are committed to continued innovation in dermatology to make a positive difference in the fight against skin cancer.

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Skin cancer: HAM10000