abhikrm0102/Lung-Nodules-Detection-and-Classification-using-UNet-DenseNet
Develop a machine learning (ML) model for lung cancer detection using U-Net and DenseNet architectures. Achieve an accuracy of at least 99.96% in lung nodule detection and classification. Achieved validation of 99.9%.
This project helps radiologists and other healthcare professionals rapidly identify and classify lung nodules in CT scans. It takes 3D computed tomography (CT) scans as input and outputs a detection and classification of lung nodules, indicating whether they are likely benign or malignant. This tool is designed for medical imaging specialists and oncologists who need to quickly and accurately assess lung health for early cancer detection.
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Use this if you are a radiologist or medical imaging specialist seeking to automate and enhance the accuracy of lung nodule detection and classification from CT scans.
Not ideal if you are looking for a diagnostic tool that replaces clinical judgment, as this is an assistive technology.
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Dec 09, 2023
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