Liver-US

The Liver-US dataset is a comprehensive collection of high-quality ultrasound images of the liver, including both normal and abnormal cases. This dataset is designed to facilitate research in medical image classification, with a focus on liver-related conditions. It includes a diverse range of ultrasound images acquired from multiple clinical settings, providing a robust foundation for developing and validating machine learning models in medical image analysis.
Detailed Dataset Description

Dataset Characteristics:
The Liver-US dataset contains735 ultrasound images of the liver, including both normal and abnormal cases. The dataset includes a variety of liver conditions, such as Hepatocellular Carcinoma (HCC), liver cysts, hemangiomas, and other focal liver lesions. The images were acquired using state-of-the-art ultrasound equipment, ensuring high resolution and contrast for accurate diagnosis.

Motivations and Content Summary:
The primary motivation behind the creation of the Liver-US dataset is to provide a comprehensive and diverse resource for researchers working on medical image classification task related to the liver. The dataset was compiled in collaboration with leading medical institutions and includes images from a diverse patient population, ensuring representativeness and generalizability. The annotations were performed by experienced radiologists, adhering to strict quality control standards to ensure accuracy and reliability.

Potential Use Cases:
Research and Development: The dataset is ideal for researchers and developers working on machine learning models for medical image classification and feature extraction.
Clinical Applications: It can be used to train and validate computer-aided diagnosis systems for early detection and diagnosis of liver conditions.
Educational Purposes: The dataset serves as a valuable resource for training medical professionals and students in the interpretation of ultrasound images and the diagnosis of liver conditions.
Benchmarking: It provides a standardized benchmark for comparing the performance of different algorithms and models in the field of medical image analysis.