Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer

Li, Yong; Yang, Caiqin; Huang, Jun; Liu, Shaopeng; Zhuo, Yumin; Lu, Xu

doi:10.1016/j.future.2020.08.015

Cited by 111 publications

(42 citation statements)

References 23 publications

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“…Several convolutional neural network architectures have been used to process images [ 30 , 31 , 32 ], including medical images, as in [ 8 , 18 , 33 , 34 ]. For this reason, this study investigated the performance of several convolutional neural network architectures for the classification of cervical cell nuclei obtained in Pap smears.…”

Section: Materials and Methodsmentioning

confidence: 99%

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

et al. 2021

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In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.

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Section: Materials and Methodsmentioning

confidence: 99%

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

et al. 2021

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“…In [27], a combined network model of LEN and CEN was proposed to segment the spine and calculate the angle. In [28], R-CNN was proposed to segment the vertebrae, and then the Cobb angle was calculated using the center points of the vertebrae. For the segmentation of spinal vertebrae, the segmented image is used to estimate the lordosis angle.…”

Section: Structure Model Of U-netmentioning

confidence: 99%

Cobb Angle Measurement Method of Scoliosis Based on U-net Network

Cui

Gao

Shen

et al. 2021

Preprint

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The Cobb angle is an important indicator for judging the severity of scoliosis. However, the segmentation and corner marking methods based on deep learning have problems such as target area segmentation and corner detection blur in the X-ray Cobb angle measurement. In this paper, a new convex hull algorithm to detect the corners and a mask generation strategy are proposed to improve the accuracy of Cobb angle recognition. On this basis, the Cobb angle measurement method is presented to identify and segment the target area based on U-net network, and then combine the new convex hull algorithm to detect corners and mask generation strategies. A total of 68 corner points were marked on 17 vertebrae, and the corner points detected by the markers were used to calculate the Cobb angle. The experimental results have proved that the U-net based measurement method could effectively improve the corner detection accuracy on the basis of segmentation, thereby reducing the calculation error of the Cobb angle. The Cobb mean absolute error (AMAE) is 9.2832°, and the symmetric mean absolute percentage error (SMAPE) is 21.675%, which achieved a relatively good result compared with the measurement by professional orthopaedist in error.

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“…In this research, AC, SE, SP, and F1 are calculated for the comparing analysis. ANN [37], k-nearest neighbor (kNN) [38], Fast region-based convolutional neural network (Fast R-CNN) [39], Visual Geometry Group -16 (VGG16) [40], Scaled Conjugate Gradient CNN (SGC-CNN) [41], GoogleNet [42], AlexNet [43], ResNet-50-177 [44], and Inception-v3 [45] were selected for comparing analysis finally. The proposed model's performance is listed in Table 2; and the results show that the dominance of the proposed improved resident network-based cGAN model (RNcGAN) in indices of A, SE, SP, and F1.…”

Section: Fig 9 Plantar Pressure Experimental Data-set Collection Anmentioning

confidence: 99%

An Improved Residual-Network Model-based Conditional Generative Adversarial Network Plantar Pressure Image Classification: A Comparison of Normal, Planus, and Talipes Equinovarus Feet

Han

Wang

et al. 2021

Preprint

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The number of layers of deep learning (DL) increases, and following the performance of computing nodes improvement, the output accuracy of deep neural networks (DNN) faces a bottleneck problem. The resident network (RN) based DNN model was applied to address these issues recently. This paper improved the RN and developed a rectified linear unit (ReLU) based conditional generative adversarial nets (cGAN) to classify plantar pressure images. A foot scan system collected the plantar pressure images, in which normal (N), planus (PL), and talipes equinovarus feet (TE) data-sets were acquired subsequently. The 9-foot types named N, PL, TE, N-PL, N-TE, PL-N, PL-TE, TE-N, and TE-PL were classified using the proposed DNN models, named resident network-based conditional generative adversarial nets (RNcGAN). It improved the RN structure firstly and the cGAN system hereafter. In the classification of plantar pressure images, the pixel-level state matrix can be direct as an input, which is different from the previous image classification task with image reduction and feature extraction. cGAN can directly output the pixels of the image without any simplification. Finally, the model achieved better results in the evaluation indicators of accuracy (AC), sensitivity (SE), and F1-measurement (F1) by comparing to artificial neural networks (ANN), k-nearest neighbor (kNN), Fast Region-based Convolution Neural Network (Fast R-CNN), visual geometry group (VGG16), scaled-conjugate-gradient convolution neural networks (SCG-CNN), GoogleNet, AlexNet, ResNet-50-177, and Inception-v3. The final prediction of class accuracy is 95.17%. Foot type classification is vital for producing comfortable shoes in the industry.

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Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer

Cited by 111 publications

References 23 publications

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

Cobb Angle Measurement Method of Scoliosis Based on U-net Network

An Improved Residual-Network Model-based Conditional Generative Adversarial Network Plantar Pressure Image Classification: A Comparison of Normal, Planus, and Talipes Equinovarus Feet

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