Author
Lei Zhao
Bio: Lei Zhao is an academic researcher. The author has contributed to research in topics: Lung cancer & Medicine. The author has an hindex of 1, co-authored 1 publications receiving 175 citations.
Topics: Lung cancer, Medicine
Papers
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TL;DR: Three types of deep neural networks are designed for lung cancer calcification and the CNN network archived the best performance with an accuracy, sensitivity, and specificity of 84.32%, which has the best result among the three networks.
Abstract: 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.
304 citations
TL;DR: Wang et al. as mentioned in this paper adopted the method of meta-analysis to estimate the effectiveness and safety of acupuncture combined with percutaneous transforaminal endoscopic discectomy (PTED) in patients with Lumbar disc herniation, in order to provide a basis for clinical decision-making.
1 citations
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TL;DR: An introduction to deep learning technology is provided and the stages that are entailed in the design process of deep learning radiology research are presented and the results of a survey of the application of convolutional neural networks to radiologic imaging are detailed.
Abstract: This article is a guide to convolutional neural network technologies and their clinical applications in the analysis of radiologic images.
287 citations
TL;DR: Basic technical knowledge regarding deep learning with CNNs along the actual course is illustrated (collecting data, implementing CNNs, and training and testing phases).
Abstract: 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.
238 citations
TL;DR: This work proposes two different DL techniques to assess the considered problem, and implements a fusion of handcrafted and learned features in the MAN to improve classification accuracy during lung cancer assessment.
228 citations
TL;DR: The concept of deep learning is explained, addressing it in the broader context of machine learning, and the most common network architectures are presented, with a more specific focus on convolutional neural networks.
214 citations
TL;DR: In this article, the authors proposed a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN, which is termed as VGG Data STN with CNN (VDSNet).
191 citations