scholarly journals Bone fracture detection through the two-stage system of Crack-Sensitive Convolutional Neural Network

2021 ◽  
Vol 22 ◽  
pp. 100452
Author(s):  
Yangling Ma ◽  
Yixin Luo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Bone Fracture ◽  
Fracture Detection ◽  
Two Stage ◽  
Stage System

Comparative Analysis of Deep Convolutional Neural Network Models for Humerus Bone Fracture Detection

2021 ◽  
Vol 11 (12) ◽  
pp. 3117-3122
Author(s):  
A. Sasidhar ◽  
M. S. Thanabal
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Bone Fracture ◽  
Medical Image ◽  
Bone Fractures ◽  
Medical Image Processing ◽  
Fracture Detection ◽  
Wide Range

Deep learning plays a key role in medical image processing. One of the applications of deep learning models in this domain is bone fracture detection from X-ray images. Convolutional neural network and its variants are used in wide range of medical image processing applications. MURA Dataset is commonly used in various studies that detect bone fractures and this work also uses that dataset, in specific the Humerus bone radiograph images. The humerus dataset in the MURA dataset contains both images with fracture and without fracture. The image with fracture includes images with metals which are removed in this work. Experimental analysis was made with two variants of convolutional neural network, DenseNet169 Model and the VGG Model. In case of the DenseNet169 model, a model with the pre trained weights of ImageNet and one without it is experimented. Results obtained with these variants of CNN are comparedand it shows that DenseNet169 model that uses pre-trained weights of ImageNet model performs better than the other two models.

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

scholarly journals Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 180618-180632
Author(s):  
Mehdi Abdollahpour ◽  
Tohid Yousefi Rezaii ◽  
Ali Farzamnia ◽  
Ismail Saad
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Sleep Stage ◽  
Two Stage ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Fusion Framework

Multi-focus image fusion method based on two stage of convolutional neural network

2020 ◽  
Vol 176 ◽  
pp. 107681
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen ◽  
Pengxiang Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Fusion Method ◽  
Two Stage ◽  
Focus Image ◽  
Image Fusion Method

scholarly journals Two-Stage Liver and Tumor Segmentation Algorithm Based on Convolutional Neural Network

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1806
Author(s):  
Lu Meng ◽  
Qianqian Zhang ◽  
Sihang Bu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Liver Tumors ◽  
Segmentation Algorithm ◽  
Tumor Segmentation ◽  
Liver Segmentation ◽  
Dice Coefficient ◽  
Long Distance ◽  
Two Stage ◽  
Public Data

The liver is an essential metabolic organ of the human body, and malignant liver tumors seriously affect and threaten human life. The segmentation algorithm for liver and liver tumors is one of the essential branches of computer-aided diagnosis. This paper proposed a two-stage liver and tumor segmentation algorithm based on the convolutional neural network (CNN). In the present study, we used two stages to segment the liver and tumors: liver localization and tumor segmentation. In the liver localization stage, the network segments the liver region, adopts the encoding–decoding structure and long-distance feature fusion operation, and utilizes the shallow features’ spatial information to improve liver identification. In the tumor segmentation stage, based on the liver segmentation results of the first two steps, a CNN model was designed to accurately identify the liver tumors by using the 2D image features and 3D spatial features of the CT image slices. At the same time, we use the attention mechanism to improve the segmentation performance of small liver tumors. The proposed algorithm was tested on the public data set Liver Tumor Segmentation Challenge (LiTS). The Dice coefficient of liver segmentation was 0.967, and the Dice coefficient of tumor segmentation was 0.725. The proposed algorithm can accurately segment the liver and liver tumors in CT images. Compared with other state-of-the-art algorithms, the segmentation results of the proposed algorithm rank the highest in the Dice coefficient.

scholarly journals Hippocampus Localization Using a Two-Stage Ensemble Hough Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73436-73447 ◽  
Author(s):  
Abol Basher ◽  
Kyu Yeong Choi ◽  
Jang Jae Lee ◽  
Bumshik Lee ◽  
Byeong C. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Two Stage

scholarly journals Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e041139
Author(s):  
Yuexin Cai ◽  
Jin-Gang Yu ◽  
Yuebo Chen ◽  
Chu Liu ◽  
Lichao Xiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tympanic Membrane ◽  
Otitis Media ◽  
Validation Study ◽  
Model Development ◽  
Classification Model ◽  
Automated Diagnosis ◽  
Two Stage ◽  
Development And Validation

ObjectivesThis study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images.DesignA classification model development and validation study in ears with otitis media based on otoscopic TM images. Two commonly used CNNs were trained and evaluated on the dataset. On the basis of a Class Activation Map (CAM), a two-stage classification pipeline was developed to improve accuracy and reliability, and simulate an expert reading the TM images.Setting and participantsThis is a retrospective study using otoendoscopic images obtained from the Department of Otorhinolaryngology in China. A dataset was generated with 6066 otoscopic images from 2022 participants comprising four kinds of TM images, that is, normal eardrum, otitis media with effusion (OME) and two stages of chronic suppurative otitis media (CSOM).ResultsThe proposed method achieved an overall accuracy of 93.4% using ResNet50 as the backbone network in a threefold cross-validation. The F1 Score of classification for normal images was 94.3%, and 96.8% for OME. There was a small difference between the active and inactive status of CSOM, achieving 91.7% and 82.4% F1 scores, respectively. The results demonstrate a classification performance equivalent to the diagnosis level of an associate professor in otolaryngology.ConclusionsCNNs provide a useful and effective tool for the automated classification of TM images. In addition, having a weakly supervised method such as CAM can help the network focus on discriminative parts of the image and improve performance with a relatively small database. This two-stage method is beneficial to improve the accuracy of diagnosis of otitis media for junior otolaryngologists and physicians in other disciplines.

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