Lung Cancer (Histopathological Images)

Dataset Overview

The LC25000 Dataset is a groundbreaking collection of 25,000 high-resolution, color-coded histological images designed to advance cancer pathology research using machine learning. This dataset addresses the critical need for diverse, high-quality, ML-ready medical image datasets to train robust computer models. It serves as an essential resource for AI researchers, especially in the field of cancer detection and diagnosis.

Key Features

• Comprehensive Classes: The dataset includes 25,000 images divided equally into 5,000 images across five categories:
  • Colon Adenocarcinoma
  • Benign Colonic Tissue
  • Lung Adenocarcinoma
  • Lung Squamous Cell Carcinoma
  • Benign Lung Tissue
• De-Identified and Compliant: All images are fully anonymized and meet HIPAA compliance standards to ensure data privacy and ethical use.
• Validated Data: Expert-reviewed to guarantee high-quality annotations and accurate classification.
• Freely Available: Open access for AI researchers to develop, test, and validate innovative machine learning models.

Applications

• Cancer Detection: Train machine learning models to accurately identify and differentiate cancerous and benign tissues.
• Medical Image Classification: Develop AI-driven diagnostic tools for histopathology.
• Research and Education: Empower researchers and educators with a rich dataset for exploring AI applications in oncology.

This dataset is sourced from Kaggle.