QuEST Lung Nodule Detection in CT scans

QuEST Lung Nodule Detection in CT scans

A new CAD system which uses fully convolutional DetectNet architecture for real time detection and localization of lung nodules in low dose CT scans. Lung cancer is the leading cause of cancer related deaths in the world. The survival rate can be improved if the presence of lung nodules are detected early. This has also led to more focus being given to computer aided detection (CAD) and diagnosis of lung nodules. The arbitrariness of shape, size and texture of lung nodules is a challenge to be faced when developing these detection systems. In the proposed work we use convolutional neural networks to learn the features for nodule detection, replacing the traditional method of handcrafting features like geometric shape or texture. The model can be deployed in CPUs. This solution plays an important role in reducing the reading time in a cost effective way.

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WorldwideIntel® Core™ Processor FamilyIntel® Xeon ScalableCaffeHealthcareModels can be trained - requires labeled dataLinux Intel® Distribution for PythonIntel® Distribution of OpenVINO™ toolkitOtherMedical imaging, analysis and diagnosticsMachine Learning