Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans

Sindhu Ramachandran S.; Jose George; Shibon Skaria; Varun V. V.

doi:10.1117/12.2293699

27 February 2018 Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans

Sindhu Ramachandran S., Jose George, Shibon Skaria, Varun V. V.

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105751I (2018) https://doi.org/10.1117/12.2293699
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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. Our network uses the DetectNet architecture based on YOLO (You Only Look Once) to detect the nodules in CT scans of lung. In this architecture, object detection is treated as a regression problem with a single convolutional network simultaneously predicting multiple bounding boxes and class probabilities for those boxes. By performing training using chest CT scans from Lung Image Database Consortium (LIDC), NVIDIA DIGITS and Caffe deep learning framework, we show that nodule detection using this single neural network can result in reasonably low false positive rates with high sensitivity and precision.

Conference Presentation

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Sindhu Ramachandran S., Jose George, Shibon Skaria, and Varun V. V. "Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751I (27 February 2018); https://doi.org/10.1117/12.2293699

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