What does the Breast Ultrasound Images Dataset include?

The dataset includes 780 PNG ultrasound images collected from 600 female patients aged 25–75. Each image is paired with a ground truth mask and categorized as normal, benign, or malignant, enabling classification and segmentation tasks.

How can the Breast Ultrasound Images Dataset be used?

It is designed for medical research, training and validating machine learning models for breast cancer detection, and educational purposes for medical professionals and AI researchers. The balanced class representation supports robust supervised learning and evaluation.

How does GTS ensure dataset quality and compliance?

GTS applies strict quality control (QC) processes, ISO 9001:2015 and ISO 27001:2013 standards, and ensures compliance with GDPR, HIPAA, and global medical data protection regulations. All datasets are carefully cleaned, validated, and anonymized before delivery.

Breast Ultrasound Images Dataset

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Breast Ultrasound Images Dataset

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Breast Ultrasound Images

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Breast Ultrasound Images

Description

Explore a high-quality breast ultrasound images dataset with 780 PNG images from 600 patients, categorized into normal, benign, and malignant.

Description:

This dataset includes breast ultrasound images collected in 2018 from 600 female patients aged between 25 and 75. The dataset contains 780 images with an average size of 500×500 pixels, stored in PNG format. Each image is paired with a ground truth image. The images are categorized into three classes: normal, benign, and malignant.

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The dataset aims to aid in the development of diagnostic tools and algorithms for breast cancer detection and classification. Each category is well-represented, providing a balanced dataset for training and evaluation. The inclusion of ground truth images ensures that the data can be used for supervised learning tasks, enabling precise model validation and improvement.

Usage and Applications:

Medical Research: To develop and validate new diagnostic tools for breast cancer detection.
Machine Learning Models: For training, testing, and improving classification algorithms in medical imaging.
Educational Purposes: To educate and train healthcare professionals and data scientists in the application of AI in medical diagnostics.

Key Features:

Diverse Age Range: Covers a wide age range, ensuring the applicability of developed models across different demographics.
High-Quality Images: PNG format with high resolution provides clear and detailed images for analysis.
Comprehensive Categorization: Includes normal, benign, and malignant images, offering a robust dataset for diverse research needs.

This dataset is sourced from Kaggle.

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Breast Ultrasound Images Dataset