Semantic segmentation using reinforced fully convolutional densenet with multiscale kernel

Brahimi, Sourour; Aoun, Najib Ben; Benoît, Alexandre; Lambert, Patrick; Amar, Chokri Ben

doi:10.1007/s11042-019-7430-x

Cited by 14 publications

(9 citation statements)

References 37 publications

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“…FC-DenseNet contains the down sampling path for extracting sparse semantic features and the up sampling path for restoring original resolution. The down sampling path consists of dense block (DB) layer and transition down (TD) layer 22 . The up sampling path consists of DB layer and transition up (TU) layer.…”

Section: Methodsmentioning

confidence: 99%

A new detection model of microaneurysms based on improved FC-DenseNet

Wang

Yao

et al. 2022

Sci Rep

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Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Microaneurysm (MA) is usually the first symptom of DR that leads to blood leakage in the retina. Periodic detection of MAs will facilitate early detection of DR and reduction of vision injury. In this study, we proposed a novel model for the detection of MAs in fluorescein fundus angiography (FFA) images based on the improved FC-DenseNet, MAs-FC-DenseNet. FFA images were pre-processed by the Histogram Stretching and Gaussian Filtering algorithm to improve the quality of FFA images. Then, MA regions were detected by the improved FC-DenseNet. MAs-FC-DenseNet was compared against other FC-DenseNet models (FC-DenseNet56 and FC-DenseNet67) or the end-to-end models (DeeplabV3+ and PSPNet) to evaluate the detection performance of MAs. The result suggested that MAs-FC-DenseNet had higher values of evaluation metrics than other models, including pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean intersection over union (MIoU). Moreover, MA detection performance for MAs-FC-DenseNet was very close to the ground truth. Taken together, MAs-FC-DenseNet is a reliable model for rapid and accurate detection of MAs, which would be used for mass screening of DR patients.

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Section: Methodsmentioning

confidence: 99%

A new detection model of microaneurysms based on improved FC-DenseNet

Wang

Yao

et al. 2022

Sci Rep

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“…Hao et al proposed a method based on convolutional sparse self-coding neural network, which has been proved to have good classification performance [8]. Nowadays, with the continuous development of deep learning technology, there are many networks with better performance and larger scale, such as Google Net [9], ResNet [10], and DenseNet [11]. Tiktok, Kwai, and today's headlines are the short video resources represented by [12], which makes it impossible to detect and extract facial expression's facial features and facial expression accurately.…”

Section: Related Workmentioning

confidence: 99%

Short Video Copyright Storage Algorithm Based on Blockchain and Expression Recognition

Yang

2022

International Journal of Digital Multimedia Broadcasting

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Blockchain technology is widely used in the field of digital right protection technology. The traditional digital right protection scheme is not only inefficient and highly centralized but also has the risk of being modified. Due to its own characteristics, blockchain cannot completely store all the original files of digital resources. In this paper, a convolutional neural network algorithm based on visual priority rule is proposed (CNNVP). This algorithm can recognize facial expressions in the original files of digital resources (for short video of face class). The algorithm extracts facial expression features accurately and makes these features form log files that can represent the original files of digital resources. Then, the paper proposes a short video copyright storage algorithm based on blockchain and facial expression recognition and stores the log file into the blockchain. The above methods not only improve the efficiency of short video copyright storage, reduce the degree of storage centralization, and eliminate the risk that copyright is easy to be modified. Moreover, the computing operation of deep learning technology on short video not only ensures the privacy of storage certificate information but also ensures the possibility of blockchain storage of video information. Experiments show that the algorithm proposed in this paper is more efficient than the traditional copyright storage method. Moreover, the algorithm proposed in this paper can provide technical support to the media resource management department.

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“…The segmentation techniques play an essential role in the diagnosis, feature extraction, and classification accuracy of breast masses as benign and malignant. Different deep learning segmentation methods are used for breast cancer images such as FCN [ 8 ], U-Net [ 9 , 26 , 27 ], Segmentation Network (SegNet) [ 28 ], Full Resolution Convolutional Network (FrCN) [ 29 ], mask Region-Based Convolutional Neural Networks mask (RCNNs) [ 11 , 30 ], Attention guided dense up-sampling network(Aunet) [ 31 ], Residual attention U-Net model (RUNet) [ 32 ], conditional Generative Adversarial Networks (cGANs) [ 33 ], Densely connected U-Net and attention gates (AGs) [ 34 ], and Conditional random field model (CRF) [ 35 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

AlEisa

Touiti

Alhussan

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from mammography images. It will identify the relevant zones to model while WAE is used to model the extracted information for these zones. In fact, WAE has proven its superiority to the majority of the features extraction approaches. The fusion of these two techniques have improved the feature extraction phase and this by keeping and modeling only the relevant and useful features for the identification and description of breast masses. The experimental results showed the effectiveness of our proposed method which has given very encouraging results in comparison with the states of the art approaches on the same mammographic image base. A precision rate of 94% for benign and 93% for malignant was achieved with a recall rate of 92% for benign and 95% for malignant. For the normal case, we were able to reach a rate of 100%.

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Semantic segmentation using reinforced fully convolutional densenet with multiscale kernel

Cited by 14 publications

References 37 publications

A new detection model of microaneurysms based on improved FC-DenseNet

A new detection model of microaneurysms based on improved FC-DenseNet

Short Video Copyright Storage Algorithm Based on Blockchain and Expression Recognition

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

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