This article is a guide to convolutional neural network technologies and their clinical applications in the analysis of radiologic images.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018180547

Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Deep learning with convolutional neural network in radiology

https://repositorio-aberto.up.pt/bitstream/10216/124569/3/369051.1.pdf

Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images

Survey on deep learning for radiotherapy.

Hybrid deep learning for detecting lung diseases from X-ray images

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.

/pdf/using-deep-learning-for-classification-of-lung-nodules-on-3uvmuasu29.pdf

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Percutaneous transforaminal endoscopic discectomy combined with acupuncture for lumbar disc herniation: A protocol for systematic review and meta-analysis

Lei Zhao

Papers

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Percutaneous transforaminal endoscopic discectomy combined with acupuncture for lumbar disc herniation: A protocol for systematic review and meta-analysis