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Lung and Colon Cancer Histopathological Image Dataset (LC25000)
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Lung and Colon Cancer Histopathological Image Dataset (LC25000)
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The field of Machine Learning, a subset of Artificial Intelligence, has led to remarkable advancements in many areas, including medicine. Machine Learning algorithms require large datasets to train computer models successfully. Although there are medical image datasets available, more image datasets are needed from a variety of medical entities, especially cancer pathology. Even more scarce are ML-ready image datasets. To address this need, we created an image dataset (LC25000) with 25,000 color images in 5 classes. Each class contains 5,000 images of the following histologic entities: colon adenocarcinoma, benign colonic tissue, lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. All images are de-identified, HIPAA compliant, validated, and freely available for download to AI researchers.
Forward citations
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Enhancing Histopathological Image Classification via Integrated HOG and Deep Features with Robust Noise Performance
Fusing HOG and deep features from fine-tuned InceptionResNet-v2 yields 99.84% accuracy and 99.99% AUC on LC25000 histopathology classification with improved noise resilience.
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