TSABCNN: Two-Stage Attention-Based Convolutional Neural Network for Frame Identification

2018 ◽  
pp. 289-301
Author(s):  
Hongyan Zhao ◽  
Ru Li ◽  
Fei Duan ◽  
Zepeng Wu ◽  
Shaoru Guo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Two Stage

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.

scholarly journals A convolutional neural network with a two-stage LSTM model for tool presence detection in laparoscopic videos

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Critical Role ◽  
Video Clip ◽  
Short Term ◽  
Two Stage ◽  
Surgical Tool ◽  
Long Short Term Memory ◽  
Laparoscopic Videos

AbstractSurgical tool presence detection in laparoscopic videos is a challenging problem that plays a critical role in developing context-aware systems in operating rooms (ORs). In this work, we propose a deep learning-based approach for detecting surgical tools in laparoscopic images using a convolutional neural network (CNN) in combination with two long short-term memory (LSTM) models. A pre-trained CNN model was trained to learn visual features from images. Then, LSTM was employed to include temporal information through a video clip of neighbour frames. Finally, the second LSTM was utilized to model temporal dependencies across the whole surgical video. Experimental evaluation has been conducted with the Cholec80 dataset to validate our approach. Results show that the most notable improvement is achieved after employing the two-stage LSTM model, and the proposed approach achieved better or similar performance compared with state-of-the-art methods.

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