visual geometry
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2021 ◽  
Vol 38 (6) ◽  
pp. 1699-1711
Author(s):  
Devanshu Tiwari ◽  
Manish Dixit ◽  
Kamlesh Gupta

This paper simply presents a fully automated breast cancer detection system as “Deep Multi-view Breast cancer Detection” based on deep transfer learning. The deep transfer learning model i.e., Visual Geometry Group 16 (VGG 16) is used in this approach for the correct classification of Breast thermal images into either normal or abnormal. This VGG 16 model is trained with the help of Static as well as Dynamic breast thermal images dataset consisting of multi-view, single view breast thermal images. These Multi-view breast thermal images are generated in this approach by concatenating the conventional left, frontal and right view breast thermal images taken from the Database for Mastology Research with Infrared image for the first time in order to generate a more informative and complete thermal temperature map of breast for enhancing the accuracy of the overall system. For the sake of genuine comparison, three other popular deep transfer learning models like Residual Network 50 (ResNet50V2), InceptionV3 network and Visual Geometry Group 19 (VGG 19) are also trained with the same augmented dataset consisting of multi-view as well as single view breast thermal images. The VGG 16 based Deep Multi-view Breast cancer Detect system delivers the best training, validation as well as testing accuracies as compared to their other deep transfer learning models. The VGG 16 achieves an encouraging testing accuracy of 99% on the Dynamic breast thermal images testing dataset utilizing the multi-view breast thermal images as input. Whereas the testing accuracies of 95%, 94% and 89% are achieved by the VGG 19, ResNet50V2, InceptionV3 models respectively over the Dynamic breast thermal images testing dataset utilizing the same multi-view breast thermal images as input.


2021 ◽  
Vol 15 (1) ◽  
pp. 180-189
Author(s):  
Shital D. Bhatt ◽  
Himanshu B. Soni

Background: Lung cancer is among the major causes of death in the world. Early detection of lung cancer is a major challenge. These encouraged the development of Computer-Aided Detection (CAD) system. Objectives: We designed a CAD system for performance improvement in detecting and classifying pulmonary nodules. Though the system will not replace radiologists, it will be helpful to them in order to accurately diagnose lung cancer. Methods: The architecture comprises of two steps, among which in the first step CT scans are pre-processed and the candidates are extracted using the positive and negative annotations provided along with the LUNA16 dataset, and the second step consists of three different neural networks for classifying the pulmonary nodules obtained from the first step. The models in the second step consist of 2D-Convolutional Neural Network (2D-CNN), Visual Geometry Group-16 (VGG-16) and simplified VGG-16, which independently classify pulmonary nodules. Results: The classification accuracies achieved for 2D-CNN, VGG-16 and simplified VGG-16 were 99.12%, 98.17% and 99.60%, respectively. Conclusion: The integration of deep learning techniques along with machine learning and image processing can serve as a good means of extracting pulmonary nodules and classifying them with improved accuracy. Based on these results, it can be concluded that the transfer learning concept will improve system performance. In addition, performance improves proper designing of the CAD system by considering the amount of dataset and the availability of computing power.


Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 69
Author(s):  
Philippe Germain ◽  
Armine Vardazaryan ◽  
Nicolas Padoy ◽  
Aissam Labani ◽  
Catherine Roy ◽  
...  

Background: Diagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators. Method: 119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators. Results: frame-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 (p < 0.0008) and an AUC of 0.630 vs. 0.824 (p < 0.0001). Patient-based comparisons provided an accuracy of 0.660 vs. 0.825 (p < 0.008) and an AUC of 0.727 vs. 0.895 (p < 0.002). Conclusion: based on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis.


2021 ◽  
Vol 10 (6) ◽  
pp. 3860-3865
Author(s):  
Adya Trisal

Food is one of the most fundamental necessities and is crucial for survival. Loss of the food source due to pest infestation attributes towards destroying one-fifth of the yearly worldwide crop yield. The past few decades have witnessed a burgeoning trend of using computerized methods for discerning various diseases found in crops. The main advantage of digitizing the detection process is that it eliminates the errors and miscalculations associated with manual detection. With the advent of Object Detection and Artificial Intelligence, malady detection has not only been rapid but has also maintained the expected level of accuracy. The concepts and models of deep learning have been efficaciously applied and used to identify as well as classify plant diseases. In the scope of this research paper, we present a comprehensive digitized approach to detect plant diseases by utilizing image detection, computer vision, and deep learning models like the Convolutional neural networks, Inception model, and the Visual Geometry Group (VGG16) model. In addition to this, the performance of the above-mentioned models has been evaluated by the virtue of metrics like f1 score, accuracy, precision, and recall.


Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2883
Author(s):  
Jie Cao ◽  
Chun Bao ◽  
Qun Hao ◽  
Yang Cheng ◽  
Chenglin Chen

The detection of rotated objects is a meaningful and challenging research work. Although the state-of-the-art deep learning models have feature invariance, especially convolutional neural networks (CNNs), their architectures did not specifically design for rotation invariance. They only slightly compensate for this feature through pooling layers. In this study, we propose a novel network, named LPNet, to solve the problem of object rotation. LPNet improves the detection accuracy by combining retina-like log-polar transformation. Furthermore, LPNet is a plug-and-play architecture for object detection and recognition. It consists of two parts, which we name as encoder and decoder. An encoder extracts images which feature in log-polar coordinates while a decoder eliminates image noise in cartesian coordinates. Moreover, according to the movement of center points, LPNet has stable and sliding modes. LPNet takes the single-shot multibox detector (SSD) network as the baseline network and the visual geometry group (VGG16) as the feature extraction backbone network. The experiment results show that, compared with conventional SSD networks, the mean average precision (mAP) of LPNet increased by 3.4% for regular objects and by 17.6% for rotated objects.


Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1995
Author(s):  
Guangjun Liu ◽  
Xiaoping Xu ◽  
Xiangjia Yu ◽  
Feng Wang

In the development of high-tech industries, graphite has become increasingly more important. The world has gradually entered the graphite era from the silicon era. In order to make good use of high-quality graphite resources, a graphite classification and recognition algorithm based on an improved convolution neural network is proposed in this paper. Based on the self-built initial data set, the offline expansion and online enhancement of the data set can effectively expand the data set and reduce the risk of deep convolution neural network overfitting. Based on the visual geometry group 16 (VGG16), residual net 34 (ResNet34), and mobile net Vision 2 (MobileNet V2), a new output module is redesigned and loaded into the full connection layer. The improved migration network enhances the generalization ability and robustness of the model; moreover, combined with the focal loss function, the superparameters of the model are modified and trained on the basis of the graphite data set. The simulation results illustrate that the recognition accuracy of the proposed method is significantly improved, the convergence speed is accelerated, and the model is more stable, which proves the feasibility and effectiveness of the proposed method.


2021 ◽  
Author(s):  
Ju-Yi Hung ◽  
Ke-Wei Chen ◽  
Chandrashan Perera ◽  
Hsu-Kuang Chiu ◽  
Cherng-Ru Hsu ◽  
...  

BACKGROUND Accurate identification and prompt referral for blepharoptosis can be challenging for general practitioners. An artificial intelligence-aided diagnostic tool could underpin decision-making. OBJECTIVE To develop an AI model which accurately identifies referable blepharoptosis automatically and to compare the AI model’s performance to a group of non-ophthalmic physicians. METHODS Retrospective 1,000 single-eye images from tertiary oculoplastic clinics were labeled by three oculoplastic surgeons with ptosis, including true and pseudoptosis, versus healthy eyelid. The VGG (Visual Geometry Group)-16 model was trained for binary classification. The same dataset was used in testing three non-ophthalmic physicians. The Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to visualize the AI model RESULTS The VGG16-based AI model achieved a sensitivity of 92% and a specificity of 88%, compared with the non-ophthalmic physician group, who achieved a mean sensitivity of 72% [Range: 68% - 76%] and a mean specificity of 82.67% [Range: 72% - 88%]. The area under the curve (AUC) of the AI model was 0.987. The Grad-CAM results for ptosis predictions highlighted the area between the upper eyelid margin and central corneal light reflex. CONCLUSIONS The AI model shows better performance than the non-ophthalmic physician group in identifying referable blepharoptosis, including true and pseudoptosis, correctly. Therefore, artificial intelligence-aided tools have the potential to assist in the diagnosis and referral of blepharoptosis for general practitioners.


AI Magazine ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 5-18
Author(s):  
Kyoung Jun Lee ◽  
Jun Woo Kwon ◽  
Soohong Min ◽  
Jungho Yoon

This paper describes how the Big Data Research Center of Kyung Hee University and Benple Inc. developed and deployed an artificial intelligence system to automate the quality management process for Frontec, an SME company that manufactures automobile parts. Various constraints, such as response time requirements and the limited computing resources available, needed to be considered in this project. Defect finders using large-scale images are expected to classify weld nuts within 0.2 s with an accuracy rate of over 95%. Our system uses Circular Hough Transform for preprocessing as well as an adjusted VGG (Visual Geometry Group) model. Our convolutional neural network (CNN) system shows an accuracy of over 99% and a response time of about 0.14 s. To embed the CNN model into the factory, we reimplemented the preprocessing modules using LabVIEW and had the classification model server communicate with an existing vision inspector. We share our lessons from this experience by explain-ing the procedure and real-world issues developing and embedding a deep learn-ing framework in an existing manufacturing environment without implementing any hardware changes.


2021 ◽  
Vol 15 ◽  
Author(s):  
Cheng Wan ◽  
Jiasheng Wu ◽  
Han Li ◽  
Zhipeng Yan ◽  
Chenghu Wang ◽  
...  

In recent years, an increasing number of people have myopia in China, especially the younger generation. Common myopia may develop into high myopia. High myopia causes visual impairment and blindness. Parapapillary atrophy (PPA) is a typical retinal pathology related to high myopia, which is also a basic clue for diagnosing high myopia. Therefore, accurate segmentation of the PPA is essential for high myopia diagnosis and treatment. In this study, we propose an optimized Unet (OT-Unet) to solve this important task. OT-Unet uses one of the pre-trained models: Visual Geometry Group (VGG), ResNet, and Res2Net, as a backbone and is combined with edge attention, parallel partial decoder, and reverse attention modules to improve the segmentation accuracy. In general, using the pre-trained models can improve the accuracy with fewer samples. The edge attention module extracts contour information, the parallel partial decoder module combines the multi-scale features, and the reverse attention module integrates high- and low-level features. We also propose an augmented loss function to increase the weight of complex pixels to enable the network to segment more complex lesion areas. Based on a dataset containing 360 images (Including 26 pictures provided by PALM), the proposed OT-Unet achieves a high AUC (Area Under Curve) of 0.9235, indicating a significant improvement over the original Unet (0.7917).


2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Dian Hong ◽  
Ying-Yi Zheng ◽  
Ying Xin ◽  
Ling Sun ◽  
Hang Yang ◽  
...  

Abstract Background Many genetic syndromes (GSs) have distinct facial dysmorphism, and facial gestalts can be used as a diagnostic tool for recognizing a syndrome. Facial recognition technology has advanced in recent years, and the screening of GSs by facial recognition technology has become feasible. This study constructed an automatic facial recognition model for the identification of children with GSs. Results A total of 456 frontal facial photos were collected from 228 children with GSs and 228 healthy children in Guangdong Provincial People's Hospital from Jun 2016 to Jan 2021. Only one frontal facial image was selected for each participant. The VGG-16 network (named after its proposal lab, Visual Geometry Group from Oxford University) was pretrained by transfer learning methods, and a facial recognition model based on the VGG-16 architecture was constructed. The performance of the VGG-16 model was evaluated by five-fold cross-validation. Comparison of VGG-16 model to five physicians were also performed. The VGG-16 model achieved the highest accuracy of 0.8860 ± 0.0211, specificity of 0.9124 ± 0.0308, recall of 0.8597 ± 0.0190, F1-score of 0.8829 ± 0.0215 and an area under the receiver operating characteristic curve of 0.9443 ± 0.0276 (95% confidence interval: 0.9210–0.9620) for GS screening, which was significantly higher than that achieved by human experts. Conclusions This study highlighted the feasibility of facial recognition technology for GSs identification. The VGG-16 recognition model can play a prominent role in GSs screening in clinical practice.


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