VGG16 Network Research Articles

Multi Class Skin Diseases Classification Based On Dermoscopic Skin Images Using Deep Learning

Skin diseases are the most common types of health illness faced by people of different age groups. The identification and classification of skin disease problems relies on highly expert doctors and high level instruments which is a time consuming process. To avoid this delay in diagnosis, an automated system is required to identify and classify this skin disease. This paper proposes a convolutional neural network based intelligent system for multi-class skin disease categorization. Three pre-trained deep learning based convolutional neural network models, VGG16, MobileNet, and Inception V3, are used in this study to classify skin disease images. The Dermoscopic image dataset HAM10000 is used for training, validating, and testing. The proposed system is designed, implemented, and tested to classify skin lesion image into one of seven categories. The implementation result of these models using HAM10000 Dataset is obtain as MobileNet accuracy 85.72%,VGG16 accuracy 73.63% and Inception V3accuracy 75.80%.

Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions.

Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as “GLS_net” to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.

Towards automated eye cancer classification via VGG and ResNet networks using transfer learning

Complex tasks such as disease diagnosis or semantic segmentation are now becoming easier to tackle in part due to increasing advances in computing and storage. This study provides an exploratory approach utilising convolutional neural networks (CNNs) to detect ocular abnormalities with an illustrative case of uveal melanoma (UM), a type of ocular cancer. In previous studies UM has been researched employing different computational techniques focusing on discriminative features using fuzzy systems, neural networks, and adaptive neuro-fuzzy systems. However, given the inheritable nature of the problem, it was decided to use CNNs with transfer learning as a promising alternative to improve the accuracy of the results. As for the main contributions, the results outperforms different state-of-the-art computational algorithms studied to detect UM, in particular improvements in sensitivity, precision and accuracy, achieving 99%, 98% and 99%, respectively. Besides, two algorithms were implemented to reduce the bias of the dataset: a data augmentation algorithm using the Gabor filter, and an algorithm to remove light spots using Navier–Stokes approach.

Fabric defect detection and classification using modified VGG network

Fabrics and garments are some of the most important utilitarian items for human beings. The textile industry is rapidly growing with various fabric models and different attractive designs. To hold up the development of the textile industry, rigid measures have to be taken to check the quality of fabric during manufacturing. Thus, skilled workers are required to screen the quality of the fabric manually. This paper focuses on designing a deep learning framework to detect various fabric types and classify the defects using artificial intelligence. The proposed work has two phases; in phase 1, the input image is preprocessed with a novel Pseudo–Convolutional Neural Network (P-CNN) having zero tunable parameters. In phase 2, a modified Convolutional Neural Network (CNN) is applied to the preprocessed image to detect and classify major fabric defects. The Convolutional Neural Network (CNN) uses appropriate hidden layers to acquire an undeniable degree of accuracy for defect classification using images. The dataset consists of five different fabric defects such as broken pick defects, pattern defects, weft yarn deformity, soiled fabrics, and plain fabric defects that are considered for training and validation of the proposed architecture. The performance of the proposed network is measured using metric parameters such as sensitivity, specificity, and accuracy. The proposed technique has high accuracy for different fabric types used for testing the creation network.

Flame Edge Detection Method Based on a Convolutional Neural Network.

In this study, an improved flame edge detector based on convolutional neural network (CNN) was proposed. The proposed method can generate edge graphs and extract edge graphs relatively effectively. Our network architecture was based on VGG16 primarily, the last two max-pooling operators and all full connection layers of the VGG16 network were deleted, and the rest was taken as the basic network. The images output by the five convolution layers were upsampled to the size of the input images and finally fused to the edge image. Error calculation and back propagation of the fusion image and label image are carried out to form a weakly supervised model. Using the open datasets BSDS500 to train the network, the ODS F-measure can reach 0.810. Various experiments were carried out on different flame and fire images, including butane–air flame, oxygen–ethanol flame, energetic material flame, and oxygen–acetylene premixed jet flame, and the infrared thermogram was also verified by our method. The results demonstrate the effectiveness and robustness of the proposed algorithm.

Adaptive CNN filter pruning using global importance metric

The depth and width of CNNs have increased over the years so as to learn a better representation of the input–output mapping of a dataset. However, a significant amount of redundancy exists among different convolutional kernels. Several methods on pruning suggest that trimming redundant parameters can produce compact structures with minor degradation in classification performance. Existing pruning methods reduce the number of filters at a uniform rate (i.e. pruning same percentage of filters from each layer) in every convolutional layer, which is suboptimal. In this paper, we conduct experiments to observe the sensitivity of each and every filter towards the final performance of the neural network. The essence of comparing filter importance on a global scale and subsequently pruning the neural network adaptively, is highlighted for the first time in this paper. Based on our observations, we propose a novel method named ‘Global Filter Importance based Adaptive Pruning (GFI-AP)’ that assigns importance scores to all filters based on how the network learns the input–output mapping of a dataset, which can then be compared across all the other convolutional filters. Our results show that non-uniform pruning achieves better compression as compared to uniform pruning. We demonstrate that GFI-AP significantly decreases the number of FLOPs (floating point operations) of VGG and ResNet networks in ImageNet and CIFAR datasets, without substantial drop in classification accuracy. GFI-AP reduces more number of FLOPs compared to existing pruning methods, for example, the ResNet50 variant of GFI-AP provides an additional 11% reduction in FLOPs over Taylor-FO-BN-72% while achieving higher accuracy.

A novel retinal image generation model with the preservation of structural similarity and high resolution

Due to data scarcity and class imbalance in medical images, the training dataset seriously affects the classification accuracy of the model. We propose a retinal image generation model based on GAN (RetiGAN). A dual-scale discriminator is operated to train the network at two scales to improve the quality of generated images. RetiGAN can better retain the semantic information of the original images under the guidance of the content loss due to the embedding of the VGG network into RetiGAN to extract the high-level semantic information of the original and the generated images. Besides, in order to enhance the details of the generated image, RetiGAN is guided to generate the retinal images with clearer edges by feeding smoothed images to the discriminator and forcing it to distinguish the smoothed from the original ones. The qualitative and quantitative analysis verifies that the generated retinal images are similar to the original ones in structure rather than simple copies. In addition, ablation experiments exhibit that the model can improve the resolution of generated images with better visibility and clearer edges. In summary, RetiGAN is superior to other retinal image generation models in the aspects of the preservation of structural similarity and high resolution.

Optimized complex object classification model: reconstructing the ISAR image of a hypersonic vehicle covered with a plasma sheath using a U-WGAN-GP framework

ABSTRACT This study aims to apply generative adversarial networks (GANs) to the effective classification of high/low resolution (HR/LR) image pairs obtained via inverse synthetic aperture radar (ISAR) for hypersonic objects covered with a plasma sheath. We propose a classification training model based on a Wasserstein GAN with a gradient penalty (U-WGAN-GP) framework, wherein a U-Net with an excellent jump connection structure is used as a generator, and a VGG-Net with high robustness is used as a discriminator, to support the reliable classification of HR/LR ISAR image pairs for the enhancement of ISAR image resolution. The WGAN-GP provides a shortcut for the gradient propagation of network parameters during the training stage through the stable encoder and decoder structure of U-Net. It inherits the echo intensity variation and position distribution characteristics of the scattering points between hypersonic objects in LR images and effectively transplants them into the generated super-resolution ISAR images, and it establishes end-to-end mapping between the HR/LR ISAR images. Moreover, the VGG-Net ensures that the generated super-resolution images are stable, controllable, and undistorted. In addition, the WGAN-GP structure combines the content and adversarial losses, optimizes the generator loss function, and uses the GP method to provide a stable and continuous training process. Finally, a traditional VGG-16 network classifier is added at the end of the training model. The proposed model is applied to an HR/LR image pair dataset of hypersonic objects. Compared with existing methods, the proposed method improved the classification and recognition accuracy from 54.4% to 81.8%.

Application of Deep Learning Based on Garbage Image Classification

In recent years, with the rapid development of economy, the country's various construction is thriving, and remarkable achievements. At the same time, resources and the environment have been seriously damaged. This phenomenon is directly related to the irrationality of garbage classification and delivery, and the contradiction between the two is becoming increasingly acute as people strongly reflect the problem of environmental pollution but do nothing about it. This paper designs a garbage image classification system based on deep learning, the main research content is to compare multiple deep learning neural network models, find the optimal classifier, develop web applications and deploy neural networks, which includes image data acquisition, image pre-processing, and comparison of VGG16, Inception, and Resnet neural network model accuracy.

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images.

(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model’s reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making.

Spectroscopic redshift determination with Bayesian convolutional networks

Astronomy is facing large amounts of data, so astronomers have to rely on automated methods to analyse them. However, automated methods might produce incorrect values. Therefore, we need to develop different automated methods and perform a consistency check to identify them. If there is a lot of labelled data, convolutional neural networks are a powerful method for any task. We illustrate the consistency check on spectroscopic redshift determination with a method based on a Bayesian convolutional neural network inspired by VGG networks. The method provides predictive uncertainties that enable us to (1.) determine unusual or problematic spectra for visual inspection; (2.) do thresholding that allows us to balance between the error of redshift predictions and coverage. We used the 12th Sloan Digital Sky Survey quasar superset as the training set for the method. We evaluated its generalisation capability on about three-quarters of a million spectra from the 16th quasar superset of the same survey. On the 16th quasar superset, the method performs better in terms of the root-mean-squared error than the most used template fitting method. Using redshift predictions of the proposed method, we identified spectra with incorrectly determined redshifts that are unrecognised quasars or were misclassified as them.

Design and implementation of shoes arrangement robot system for unknown environment

In recent years, the robot and artificial intelligence technology have developed rapidly, but there still remains a challenging problem to realize the contact operation of robot in home environment. In order to make the robot arrange shoes automatically, we design an autonomous shoes arrangement robot system based on the 3D vision. In this system, the instance segmentation network and minimum enclosing rectangle are used to recognize the shoes and their orientation, and the grasping pose and placing pose of robot are estimated accurately by means of the point cloud information of depth camera. In addition, the convolution neural network and cosine similarity are adopted to match with a pair of shoes. Afterwards, we evaluate the accuracy of shoe orientation recognition and shoe matching in the system, and then carry out a real robot arrangement experiment. The result show that this method could assure 96.2% accuracy of shoe orientation recognition, and the matching accuracy of shoes increases from 62.6% to 87.4% when the VGG16 network is added to the shoes matching algorithm. In conclusion, this method can accurately recognize the shoes and their orientation, and then match with a pair of shoes, meanwhile, improves the stability of the robot shoes arrangement.

Water photovoltaic plant contaminant identification using visible light images

The rapid advancement of photovoltaic (PV) technology offers a significant way to advance renewable energy. As a form of PV power generation that does not take up land resources to enhance the total amount of PV power generation, water PV shows excellent development prospects. However, water PV plants lack efficient monitoring solutions for PV panels. This paper proposes a method of using visible light images (RGB colour space) captured by UAVs to automatically segment PV panels and automatically classify contaminated PV panels. A dynamic threshold segmentation method combining grey space and the S component (in HSV colour space) is proposed. Then, the texture features and pixel features of the PV panels are considered for automatic detection. In the automatic classification phase, VGG16, VGG19, ResNet50, MobileNet V2, Xception and DenseNet121 networks are evaluated with transfer learning as a prerequisite. Data augmentation techniques considering texture features are used. In these networks, VGG16 obtains the best values for accuracy, recall, F1 score and balance accuracy. In particular, the model in this paper provides visual localization information and visual classification information. In the actual test phase, the recognition accuracy of the developed method for PV panels in visible light images is 96.5%. The accuracy of the classification is 86.8%, and the recall is 76.2%. These results prove the feasibility of this method and can provide guidance for the daily maintenance of water PV power plants.

Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application

In recent years, convolutional neural network (CNN)-based algorithms have been widely used in remote sensing image processing and show tremendous performance in a variety of application fields. However, large amounts of data and intensive computations make the deployment of CNN-based algorithms a challenging problem, especially for the spaceborne scenario where resources and power consumption are limited. To tackle this problem, this paper proposes an automatic CNN deployment solution on resource-limited field-programmable gate arrays (FPGAs) for spaceborne remote sensing applications. Firstly, a series of hardware-oriented optimization methods are proposed to reduce the complexity of the CNNs. Secondly, a hardware accelerator is designed. In this accelerator, a reconfigurable processing engine array with efficient convolutional computation architecture is used to accelerate CNN-based algorithms. Thirdly, to bridge the optimized CNNs and hardware accelerator, a compilation toolchain is introduced into the deployment solution. Through the automatic conversion from CNN models to hardware instructions, various networks can be deployed on hardware in real-time. Finally, we deployed an improved VGG16 network and an improved YOLOv2 network on Xilinx AC701 to evaluate the effectiveness of the proposed deployment solution. The experiments show that with only 3.407 W power consumption and 94 DSP consumption, our solution achieves 23.06 giga operations per second (GOPS) throughput in the improved VGG16 and 22.17 GOPS throughput in the improved YOLOv2. Compared to the related works, the DSP efficiency of our solution is improved by 1.3–2.7×.

Convolutional Neural Networks for Herb Identification: Plain Background and Natural Environment

Convolutional neural networks have achieved success in resolving object identification problems. This study contributes a suitable new approach to herb identification for educational and research purposes based on a small dataset and small-sized images. Two self-collected Thai herb datasets with either plain or natural environment backgrounds were used for experimentation to realize this objective. The plain background dataset includes 4,400 images of 11 leaf types, and the natural dataset contains 1,620 images of nine leaf types. The images were divided into a training set containing 75% of the images and a separate test set with the remaining 25%. The experiments included five-fold cross-validation applied to the training set; the InceptionV3, MobileNetV2, ResNet50V2, VGG16, and Xception convolutional neural network models RMSprop and Adam optimizers. Further, dropout rates of 0.3, 0.5, and 0.7 were considered along with five and ten epochs. Transfer learning was applied using pre-trained weights. The model with the best outcome, based on the average accuracy of the cross-validation results on both datasets (the plain background dataset was 94.55%, and the natural dataset was 90.37%), was the VGG16 with the RMSprop optimizer, which exhibited a dropout rate of 0.5 over ten epochs. The model achieved 96.64% and 92.00% accuracy on the plain background training and test sets, and 99.59% and 91.36% on the natural environment training and test sets, respectively. The results show that the method has a high potential for objective tasks and application in identifying herbs based on visual leaf information.

A Survey on Human Cancer Categorization Based on Deep Learning.

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

Sentiment Analysis of Image with Text Caption using Deep Learning Techniques.

People are actively expressing their views and opinions via the use of visual pictures and text captions on social media platforms, rather than just publishing them in plain text as a consequence of technical improvements in this field. With the advent of visual media such as images, videos, and GIFs, research on the subject of sentiment analysis has expanded to encompass the study of social interaction and opinion prediction via the use of visuals. Researchers have focused their efforts on understanding social interaction and opinion prediction via the use of images, such as photographs, films, and animated GIFs (graphics interchange formats). The results of various individual studies have resulted in important advancements being achieved in the disciplines of text sentiment analysis and image sentiment analysis. It is recommended that future studies investigate the combination of picture sentiment analysis and text captions in more depth, and further research is necessary for this field. An intermodal analysis technique known as deep learning-based intermodal (DLBI) analysis is discussed in this suggested study, which may be used to show the link between words and pictures in a variety of scenarios. It is feasible to gather opinion information in numerical vector form by using the VGG network. Afterward, the information is transformed into a mapping procedure. It is necessary to predict future views based on the information vectors that have been obtained thus far, and this is accomplished through the use of active deep learning. A series of simulation tests are being conducted to put the proposed mode of operation to the test. When we look at the findings of this research, it is possible to infer that the model outperforms and delivers a better solution with more accuracy and precision, as well as reduced latency and an error rate, when compared to the alternative model (the choice).

A Violent Video Detection Method Based on Image Semantic Segmentation

With the rapid growth of the network user base and the number of short videos, a large number of videos related to terrorism and violence have emerged in the Internet, which has brought great challenges to the governance of the network environment. At present, most short-video platforms still adopt manual-review and user-report mechanisms to filter videos related to terrorism and violence, which cannot adapt to the development trend of short-video business in terms of recognition accuracy and timeliness. In the single-mode recognition method of violent video, this paper mainly studies the scene recognition mode. Firstly, the U-Net network is improved with the SE-block module. After pretraining on the Cityscapes dataset, semantic segmentation of video frames is carried out. On this basis, semantic features of scenes are extracted using the VGG16 network loaded with ImageNet pretraining weights. SE-U-Net-VGG16 scene recognition model is constructed. The experimental results show that the prediction accuracy of SE-U-Net model is much higher than that of the FCN model and U-Net model. SE-U-Net model has significant advantages in the modal research of scene recognition.

VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs.

Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. Although the accuracy achieved through CNN is good enough but this models performance degrades when there are variations in image characteristics like: rotation, tiling, and other abnormal image orientations. CNN does not store relative spatial relationships among features in scanned images. As CT scans have high spatial resolution and are sensitive to misalignments during the scanning process, there is a requirement of a technique which helps in considering spatial information of image features also. In this paper, a hybrid model named VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet). VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNeT is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98, 97.97, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.

Identification of Aircraft Wake Vortex Based on VGGNet

The exploration of information for aircraft wake vortex enables us to obtain new knowledge of wake turbulence separation standards. Traditional manual methods cannot work satisfactorily for the identification of great number of wake vortex data with high accuracy. Fortunately, the LiDAR intensity data can be explained by integrating LiDAR products with the strategies of computer vision. To overcome the limitation of traditional manual methods, this paper is aimed at developing an automatic method to identify a given set of wake vortices from various aircrafts. The main innovation works are outlined as follows. (1) From the wake vortex data that consists of various aircrafts measured by Wind3D 6000 LiDAR, a grayscale dataset of wake flow is constructed to boost the deep learning model for identifying aircraft wake vortex. (2) Following this, we propose a new method for the identification of aircraft wake vortex by modifying the VGG16 network, providing binary classifications of uncertain behavior patterns for wake vortices. To evaluate the proposed identification model, performance evaluation was conducted on our dataset, where experimental results revealed the values of 0.984, 0.951, 0.959, and 0.955 in terms of accuracy, precision, recall, and F1-score, respectively.