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Deep Learning for Lung Abnormality Classification by harshikaduhan is a document available to read on EtoBox.

What is Deep Learning for Lung Abnormality Classification about?

This study compares the performance of two deep learning algorithms, ResNet-50 and EfficientNet-B0, in classifying various lung abnormalities using chest X-ray images from multi-centric datasets. The algorithms were trained on datasets from Kaggle and Mendeley, achieving high accuracy rates of 0.9807 and 0.9874, respectively, with EfficientNet-B0 outperforming ResNet-50. The findings highlight the potential of deep learning in automating and improving the diagnostic process for lung diseases.

Author

harshikaduhan

Language

EN