scholarly journals Integrated Image Sensor and Light Convolutional Neural Network for Image Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Cheng-Jian Lin ◽  
Chun-Hui Lin ◽  
Shyh-Hau Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Autonomous Vehicles ◽  
Model Performance ◽  
Image Sensor ◽  
Model Complexity ◽  
Resource Constrained ◽  
Smart Cameras ◽  
Resource Limited ◽  
Constrained Environments

Deep learning has accomplished huge success in computer vision applications such as self-driving vehicles, facial recognition, and controlling robots. A growing need for deploying systems on resource-limited or resource-constrained environments such as smart cameras, autonomous vehicles, robots, smartphones, and smart wearable devices drives one of the current mainstream developments of convolutional neural networks: reducing model complexity but maintaining fine accuracy. In this study, the proposed efficient light convolutional neural network (ELNet) comprises three convolutional modules which perform ELNet using fewer computations, which is able to be implemented in resource-constrained hardware equipment. The classification task using CIFAR-10 and CIFAR-100 datasets was used to verify the model performance. According to the experimental results, ELNet reached 92.3% and 69%, respectively, in CIFAR-10 and CIFAR-100 datasets; moreover, ELNet effectively lowered the computational complexity and parameters required in comparison with other CNN architectures.

scholarly journals IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5541 ◽  
Author(s):  
Tom Lawrence ◽  
Li Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Computational Cost ◽  
Resource Constrained ◽  
Traffic Sign ◽  
Constrained Environments ◽  
Scale Down ◽  
Art Performance ◽  
Iot Devices

Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43% with 39% fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49% with 31.8% fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5% with 0.38% fewer FLOPs.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1688
Author(s):  
Luqman Ali ◽  
Fady Alnajjar ◽  
Hamad Al Jassmi ◽  
Munkhjargal Gochoo ◽  
Wasif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Concrete Structures ◽  
Model Performance ◽  
Training Data ◽  
Computational Time ◽  
Data Heterogeneity ◽  
Public Datasets ◽  
Detection And Localization

This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.

scholarly journals Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis

2021 ◽  
Author(s):  
Robert J. O’Shea ◽  
Amy Rose Sharkey ◽  
Gary J. R. Cook ◽  
Vicky Goh
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Model Performance ◽  
Network Models ◽  
Imaging Studies ◽  
Neural Network Models ◽  
Eligibility Criteria ◽  
Data Partitions

Abstract Objectives To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis. Methods A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied. Results One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21–34%), 31% reported demographics for their study population (58/186, 95% CI 25–39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42–57%). Median CLAIM compliance was 0.40 (IQR 0.33–0.49). Compliance correlated positively with publication year (ρ = 0.15, p = .04) and journal H-index (ρ = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001). Conclusions Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis. Key Points • Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics. • Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions. • Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.

scholarly journals Attention-mechanism-based tracking method for intelligent Internet of vehicles

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880594 ◽  
Author(s):  
Xu Kang ◽  
Bin Song ◽  
Jie Guo ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Sensor ◽  
Attention Mechanism ◽  
Vehicle Tracking ◽  
Background Region ◽  
Internet Of Vehicles ◽  
Tracking Method ◽  
Siamese Network ◽  
Selection Of

Vehicle tracking task plays an important role on the Internet of vehicles and intelligent transportation system. Beyond the traditional Global Positioning System sensor, the image sensor can capture different kinds of vehicles, analyze their driving situation, and can interact with them. Aiming at the problem that the traditional convolutional neural network is vulnerable to background interference, this article proposes vehicle tracking method based on human attention mechanism for self-selection of deep features with an inter-channel fully connected layer. It mainly includes the following contents: (1) a fully convolutional neural network fused attention mechanism with the selection of the deep features for convolution; (2) a separation method for template and semantic background region to separate target vehicles from the background in the initial frame adaptively; (3) a two-stage method for model training using our traffic dataset. The experimental results show that the proposed method improves the tracking accuracy without an increase in tracking time. Meanwhile, it strengthens the robustness of algorithm under the condition of the complex background region. The success rate of the proposed method in overall traffic datasets is higher than Siamese network by about 10%, and the overall precision is higher than Siamese network by 8%.

scholarly journals Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brain

2020 ◽  
Author(s):  
Carla Sendra-Balcells ◽  
Ricardo Salvador ◽  
Juan B. Pedro ◽  
M C Biagi ◽  
Charlène Aubinet ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Minimally Conscious State ◽  
Computation Time ◽  
Ground Truth ◽  
Electrical Current ◽  
Transcranial Electrical Stimulation ◽  
Field Calculation ◽  
Good Definition

AbstractThe segmentation of structural MRI data is an essential step for deriving geometrical information about brain tissues. One important application is in transcranial electrical stimulation (e.g., tDCS), a non-invasive neuromodulatory technique where head modeling is required to determine the electric field (E-field) generated in the cortex to predict and optimize its effects. Here we propose a deep learning-based model (StarNEt) to automatize white matter (WM) and gray matter (GM) segmentation and compare its performance with FreeSurfer, an established tool. Since good definition of sulci and gyri in the cortical surface is an important requirement for E-field calculation, StarNEt is specifically designed to output masks at a higher resolution than that of the original input T1w-MRI. StarNEt uses a residual network as the encoder (ResNet) and a fully convolutional neural network with U-net skip connections as the decoder to segment an MRI slice by slice. Slice vertical location is provided as an extra input. The model was trained on scans from 425 patients in the open-access ADNI+IXI datasets, and using FreeSurfer segmentation as ground truth. Model performance was evaluated using the Dice Coefficient (DC) in a separate subset (N=105) of ADNI+IXI and in two extra testing sets not involved in training. In addition, FreeSurfer and StarNEt were compared to manual segmentations of the MRBrainS18 dataset, also unseen by the model. To study performance in real use cases, first, we created electrical head models derived from the FreeSurfer and StarNEt segmentations and used them for montage optimization with a common target region using a standard algorithm (Stimweaver) and second, we used StarNEt to successfully segment the brains of minimally conscious state (MCS) patients having suffered from brain trauma, a scenario where FreeSurfer typically fails. Our results indicate that StarNEt matches FreeSurfer performance on the trained tasks while reducing computation time from several hours to a few seconds, and with the potential to evolve into an effective technique even when patients present large brain abnormalities.

scholarly journals Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5569
Author(s):  
Jamil Ahmad ◽  
Bilal Jan ◽  
Haleem Farman ◽  
Wakeel Ahmad ◽  
Atta Ullah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Plant Diseases ◽  
Field Conditions ◽  
Disease Detection ◽  
Resource Constrained ◽  
Extensive Data ◽  
Resource Constrained Devices ◽  
Constrained Devices

The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices.

scholarly journals A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients

2019 ◽  
Vol 60 (5) ◽  
pp. 685-693 ◽  
Author(s):  
Tomohiro Kajikawa ◽  
Noriyuki Kadoya ◽  
Kengo Ito ◽  
Yoshiki Takayama ◽  
Takahito Chiba ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Convolutional Neural Network ◽  
Cancer Patients ◽  
Dose Distribution ◽  
Organs At Risk ◽  
Model Performance ◽  
Estimation Algorithm ◽  
Neural Network Approach ◽  
Dose Distributions

Abstract The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose–volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.

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