Deep Learning for the Classification of Lung Nodules

11/21/2016
by   He Yang, et al.
0

Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature extractor, and has shown a superior performance in many visual object recognition applications. In this study, we develop a deep convolutional neural network (CNN) and apply it to thoracic CT images for the classification of lung nodules. We present the CNN architecture and classification accuracy for the original images of lung nodules. In order to understand the features of lung nodules, we further construct new datasets, based on the combination of artificial geometric nodules and some transformations of the original images, as well as a stochastic nodule shape model. It is found that simplistic geometric nodules cannot capture the important features of lung nodules.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/10/2023

LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model

The most deadly and life-threatening disease in the world is lung cancer...
research
08/10/2021

The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data

Convolutional Neural Networks (CNNs) are widely used for image classific...
research
03/01/2019

Lung CT Imaging Sign Classification through Deep Learning on Small Data

The annotated medical images are usually expensive to be collected. This...
research
04/06/2021

In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays

Artificial intelligence (AI) is disrupting the medical field as advances...
research
04/01/2019

Controlling for Biasing Signals in Images for Prognostic Models: Survival Predictions for Lung Cancer with Deep Learning

Deep learning has shown remarkable results for image analysis and is exp...

Please sign up or login with your details

Forgot password? Click here to reset