VGG16 Model Research Articles

Research on deep learning models and data preprocessing techniques for driver fatigue detection

Detecting signs of fatigue while driving is one of the most important and urgent issues in today's modern transportation system. Early detection and warning will significantly reduce the risk of traffic accidents. To accomplish this, several methods have been proposed. In recent years, methods based on deep learning techniques have attracted great attention due to their high efficiency and cost savings in detecting and warning about signs of fatigue while driving. In this paper, we study a method to improve the accuracy of deep learning models based on the data preprocessing technique, applied to detect signs of fatigue while driving. Data preprocessing helps the training images become more accurate, noise-reduced, diverse and rich instead of just using the main image. To ensure high reliability and accuracy, the methods are trained and tested on large datasets including thousands of images of all types. Experimental results show that the combination of the VGG-16 model and the data augmentation technique gives better results than the traditional deep learning model, with an accuracy of over 96%.

Optimization of the VGG Deep Learning Model Performance for Covid-19 Detection Using CT-Scan Images

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) causes a pneumonia-like disease known as Coronavirus Disease 2019 (COVID-19). The Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is the current standard for detecting COVID-19. However, CT scans can be applied for radiological inspection to detect infections in their earliest lung stages. Machine learning, specifically deep learning, can potentially speed up the evaluation of CT scan diagnoses of COVID-19. To date, no studies have been discovered that employ SGD, Adamax, or AdaGrad optimization methods with deep learning VGG model variants for COVID-19 detection in CT scan images with datasets comprising 2,038 images. This study aims to assess and compare the performance of various optimization methods for detecting COVID-19 utilizing variations of the VGG-16 and VGG-19 models based on CT scan images. Results from performance optimization comparison tests employing two VGG deep learning models were obtained, demonstrating the influence of optimization methods on model performance. The Adamax optimization method applied to the VGG-16 model performance achieved an average accuracy of 94.11% in COVID-19 detection using CT scan images, while the Adamax optimization method applied to the VGG-19 model performance achieved an average accuracy of 93.77%.

Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI.

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.

A method for disturbance identification in power quality based on cross-attention fusion of temporal and spatial features

The rapid growth of energy storage scale in power systems has posed higher demands on the monitoring and classification of power quality disturbances (PQDs). Therefore, a method based on cross-attention fusion of temporal and spatial features is proposed to improve the accuracy and robustness of fine classification of PQDs in this study.Firstly, utilizing the improved Sample Convolution and Interaction Model (SCINet) with its recursive downsampling-convolution-interaction architecture, and adding convolutional pooling layers to extract the temporal features of PQDs.Then, the improved VGG model is employed to extract the spatial features of PQDs. Finally, by introducing a cross-attention mechanism to capture the correlation and interaction between the temporal and spatial features, the representation ability of the features is enhanced to achieve disturbance recognition. This study is based on IEEE standards and mathematical models, and generates 25 types of PDQ data through Python simulation for training and testing.The proposed method achieves an average classification accuracy of 95.01 % in a high-noise environment of 20 dB, as well as compared with other mainstream deep learning models and verified through ablation study. Furthermore, the classification test of 10 types of PQDs sampled on a hardware platform demonstrates an average accuracy of 99.66 %, further validating the reliability of the proposed method.

Automated Diagnosis of Covid-19 and Pneumonia Using Transfer Learning and Custom Segmentation on Chest X-Ray Images

The key goals of this study are to discover, demonstrate, and quantify advancements in deep learning approaches for classifying healthy, pneumonia of the community-acquired and viral types, and COVID-infected lungs from X-ray images and to learn how the pre-trained models react to the training with custom segmented images. The proposed model uses the dataset pre-processed to generate unique masks and segment the lung region to train a convolutional neural network with a transfer learning model using VGG16 and VGG19 architecture. The accuracy and F1 score results for 3-way classification with custom processing are high for VGG19 with custom segmentation. In contrast, the results for the 4-way classification were stable with and without custom processing for both VGG16 and VGG19 models.

Research on image recognition of tomato leaf diseases based on improved AlexNet model

Aiming at the problems that the traditional image recognition technology is challenging to extract useful features and the recognition time is extended; the AlexNet model is improved to improve the effect of image classification and recognition. This study focuses on 8 types of tomato leaf diseases and healthy leaves. By using HOG and LBP weighted fusion to extract image features, a tomato leaf disease recognition model based on the AlexNet model is proposed, and transfer learning is used to train the AlexNet model. Transfer the knowledge learned by the AlexNet model on the PlantVillage image dataset to this model while reducing the number of fully connected layers. Keras deep learning framework and programming language Python were used. The model was implemented, and the classification and identification of tomato leaf diseases were carried out. The recognition rate of feature-weighted fusion classification is higher than that of serial and parallel methods, and the recognition time is the shortest. When the weight coefficient ratio of HOG and LBP is 3:7, the image recognition rate is the highest, and its value is 97.2 %. From the model performance curve See, when the number of iterations is more than 150 times, the training set and test accuracy rate both exceed 97 %, the loss rate shows a gradient decline, and the change is relatively stable; compared with the traditional AlexNet model, HOG + LBP + SVM model, and VGG model, improved AlexNet model has the highest recognition rate, and it has high recall value, accuracy, and F1 value; Compared with the latest convolutional neural network disease recognition models, improved AlexNet model recognition accuracy was 98.83 %, and the F1 value was 0.994. It shows that the model has good convergence performance, fast prediction speed, and low loss rate and can effectively identify 8 types of tomato leaf images, which provides a reference for the research on crop disease identification.

Classification of Apple Color and Deformity Using Machine Vision Combined with CNN

Accurately classifying the quality of apples is crucial for maximizing their commercial value. Deep learning techniques are being widely adopted for apple quality classification tasks, achieving impressive results. While existing research excels at classifying apple variety, size, shape, and defects, color and deformity analysis remain an under-explored area. Therefore, this study investigates the feasibility of utilizing convolutional neural networks (CNN) to classify the color and deformity of apples based on machine vision technology. Firstly, a custom-assembled machine vision system was constructed for collecting apple images. Then, image processing was performed to extract the largest fruit diameter from the 45 images taken for each apple, establishing an image dataset. Three classic CNN models (AlexNet, GoogLeNet, and VGG16) were employed with parameter optimization for a three-category classification task (non-deformed slice–red apple, non-deformed stripe–red apple, and deformed apple) based on apple features. VGG16 achieved the best results with an accuracy of 92.29%. AlexNet and GoogLeNet achieved 91.66% and 88.96% accuracy, respectively. Ablation experiments were performed on the VGG16 model, which found that each convolutional block contributed to the classification task. Finally, prediction using VGG16 was conducted with 150 apples and the prediction accuracy was 90.50%, which was comparable to or better than other existing models. This study provides insights into apple classification based on color and deformity using deep learning methods.

Neural Network Classification Algorithm Based on Self-attention Mechanism and Ensemble Learning for MASLD Ultrasound Images

BackgroundUltrasound image examination has become the preferred choice for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD) due to its non-invasive nature. Computer-aided diagnosis (CAD) technology can assist doctors in avoiding deviations in the detection and classification of MASLD. MethodWe propose a hybrid model that integrates the pre-trained VGG16 network with an attention mechanism and a stacking ensemble learning model, which is capable of multi-scale feature aggregation based on the self-attention mechanism and multi-classification model fusion (Logistic regression, random forest, support vector machine) based on stacking ensemble learning. The proposed hybrid method achieves four classifications of normal, mild, moderate, and severe fatty liver based on ultrasound images. Result and conclusionOur proposed hybrid model reaches an accuracy of 91.34% and exhibits superior robustness against interference, which is better than traditional neural network algorithms. Experimental results show that, compared with the pre-trained VGG16 model, adding the self-attention mechanism improves the accuracy by 3.02%. Using the stacking ensemble learning model as a classifier further increases the accuracy to 91.34%, exceeding any single classifier such as LR (89.86%) and SVM (90.34%) and RF (90.73%). The proposed hybrid method can effectively improve the efficiency and accuracy of MASLD ultrasound image detection.

Research on Internal Damage Identification of Wire Rope Based on Improved VGG Network.

In order to solve the problem of great difficulty in detecting the internal damage of wire rope, this paper proposes a method to improve the VGG model to identify the internal damage of wire rope. The short-time Fourier transform method is used to transform the wire rope damage signal into a time-frequency spectrogram as the model input, and then the traditional VGG model is improved from three aspects: firstly, the attention mechanism module is introduced to increase the effective feature weights, which effectively improves the recognition accuracy; and then, the batch normalization layer is added to carry out a uniform normalization of the data, so as to make the model easier to converge. At the same time, the pooling layer and the fully connected layer are improved to solve the redundancy problem of the traditional VGG network model, which makes the model structure more lightweight, greatly saves the computational cost, shortens the training time, and finally adopts the joint-sample uniformly distributed cross-entropy as the loss function to solve the overfitting problem and further improve the recognition rate. The experimental results show that the improved VGG model has an identification accuracy of up to 98.84% for the internal damage spectrogram of the wire rope, which shows a good identification ability. Not only that, but the model is also superior, with less time-consuming training and stronger generalization ability.

Classification of Marine Fish Based on Image Using Convolutional Neural Network Algorithm and VGG16

Marine fish are one of the many natural resources that frequently consumed by people. However, several types of marine fish are prohibited from consumption due to being nearly extinct. Additionally, some fish species contain high levels of mercury that can be harmful to human if consumed. Because of the large number of marine fish species, it becomes challenging to identify them without knowledge of fisheries. Moreover, computers have become highly advanced devices that facilitate various human activities. This advancement allows for the creation of systems capable of processing information from images, known as image classification. There are numerous methods that can be employed in designing an image classification system, one of which is transfer learning. This study was aimed to design an image classification system using the transfer learning method with a pre-trained VGG16 model and Convolutional Neural Network algorithm. The research results showed a training data accuracy of 100% and a validation data accuracy of 99.3%, with an overall accuracy of 84% and a loss value of 0.6591.

Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection

Alzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. Early diagnosis of AD is a suitable action for timely intervention and medication, which may increase the prognosis and quality of life for affected individuals. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. The full potential of quantum computing is not applied to Alzheimer’s disease classification tasks as expected. In this study, we proposed an ensemble deep learning model based on quantum machine learning classifiers to classify Alzheimer’s disease. The Alzheimer’s disease Neuroimaging Initiative I and Alzheimer’s disease Neuroimaging Initiative II datasets are merged for the AD disease classification. We combined important features extracted based on the customized version of VGG16 and ResNet50 models from the merged images then feed these features to the Quantum Machine Learning classifier to classify them as non-demented, mild demented, moderate demented, and very mild demented. We evaluate the performance of our model by using six metrics; accuracy, the area under the curve, F1-score, precision, and recall. The result validates that the proposed model outperforms several state-of-the-art methods for detecting Alzheimer’s disease by registering an accuracy of 99.89 and 98.37 F1-score.

GastroVRG: Enhancing early screening in gastrointestinal health via advanced transfer features

The accurate classification of endoscopic images is a challenging yet critical task in medical diagnostics, which directly affects the treatment and management of Gastrointestinal diseases. Misclassification can lead to incorrect treatment plans, adversely affecting patient outcomes. To address this challenge, our research aimed to develop a reliable computational model to improve the accuracy of classifying conditions of esophagitis and polyps. We focused on a subset of the Kvasir v1 secondary dataset, comprising 2000 endoscopic images evenly distributed across two classes: esophagitis and polyp. The goal was to leverage the strengths of both Machine Learning(ML) and Deep Learning(DL) to create a model that not only predicts with high accuracy but also integrates seamlessly into clinical workflows. To this end, we introduced a novel VRG-based ensemble image feature extraction technique, combining the powers of VGG, RF, and GB models to synthesize a robust feature set conducive to high-precision classification. The ensemble approach demonstrated a best-in-class performance with the GB model achieving an outstanding 99.73% accuracy in detecting esophagitis and polyps. The practical implications of these results are substantial, indicating that our method can significantly improve diagnostic accuracy in real-world settings, reduce the rate of misdiagnosis, and contribute to the efficient and effective treatment of patients, ultimately enhancing the quality of healthcare services. With the successful application of our proposed method to a controlled dataset, future work involves deploying the model in clinical environments and expanding its application to a broader spectrum of Gastrointestinal conditions across multi-class datasets.

Fault classification of photovoltaic module infrared images based on transfer learning and interpretable convolutional neural network

In the operation of photovoltaic (PV) power plants, infrared cameras are commonly utilized for monitoring the operational status of PV modules. This study focuses on the performance improvement and complexity reduction of convolutional neural network (CNN) when used for fault classification based on infrared images of PV module. By implementing the transfer learning strategy on some famous CNN models, it is observed that the number of convolutional layers has weak impact on the classification results. Therefore, a transfer-learning-based depth reduction approach for CNN models (TLDR-CNN approach) is proposed, and the VGG16 model is employed for verification. Then, a multi-scale feature extraction module (MSFE module) is developed for efficiently replacing the convolutional layers to reduce model complexity and improve classification performance, and several representative model configurations are employed for convolutional layer replacement. Experimental results demonstrate that the application of the developed MSFE module significantly outperforms the baseline model on both classification performance and model complexity. Specifically, the modified model with a reduction of 5 convolutional layers exhibits notable improvements over the training results, with an accuracy increase of 0.90%, precision increase of 0.98%, F1 score increase of 6.89%, and a Matthews correlation coefficient increase of 1.01%. Finally, the interpretability of the above outperformance is also provided by using the Grad-CAM method. The generated CAM images show that the modified model concentrates its weights more on the regions crucial for the model to learn, so the features can be extracted more efficiently.

VeriCNN: Integrity verification of large-scale CNN training process based on zk-SNARK

Cloud environments are frequently employed to train Convolutional Neural Networks (CNNs). On the other hand, the training procedure is exposed to potential integrity threats due to the cloud’s lack of trustworthiness. Although there are now available methods for zero-knowledge proof-based training integrity verification for small models, the low-proof performance of large-scale CNNs like LeNet-5 and VGG16 makes this task challenging. To solve the training integrity verification of large-scale CNNs, this research suggests a technique called VeriCNN. Particularly, VeriCNN builds proof of integrity for CNN training using zk-SNARK. It adopts the Winograd algorithm to optimize convolution operations and introduces a high-probability matrix multiplication to optimize zero-knowledge proofs. Experimental results demonstrate the effectiveness and practicality of VeriCNN in verifying the training integrity of large-scale models. For instance, VeriCNN can generate a training integrity proof for the LeNet-5 model in just 25 s and for the VGG16 model in 390 s.

Preserving authenticity: transfer learning methods for detecting and verifying facial image manipulation

Facial retouching in supporting documents can have adverse effects, undermining the credibility and authenticity of the information presented. This paper presents a comprehensive investigation into the classification of retouched face images using a fine-tuned pre-trained VGG16 model. We explore the impact of different train-test split strategies on the performance of the model and also evaluate the effectiveness of two distinct optimizers. The proposed fine-tuned VGG16 model with “ImageNet” weight achieves a training accuracy of 99.34 % and a validation accuracy of 97.91 % over 30 epochs on the ND-IIITD retouched faces dataset. The VGG16_Adam model gives a maximum classification accuracy of 96.34 % for retouched faces and an overall accuracy of 98.08 %. The experimental results show that the 50 % - 25 % train-test split ratio outperforms other split ratios mentioned in the paper. The demonstrated work shows that using a Transfer Learning approach reduces computational complexity and training time, with a max. training duration of 39.34 min for the proposed model.

Maturity grading of jujube for industrial applications harnessing deep learning

PurposeTo develop and examine an efficient and reliable jujube grading model with reduced computational time, which could be utilized in the food processing and packaging industries to perform quick grading and pricing of jujube as well as for the other similar types of fruits.Design/methodology/approachThe whole process begins with manual analysis and collection of four jujube grades from the jujube tree, in addition to this jujube image acquisition was performed utilizing MVS which is further followed by image pre-processing and augmentation tasks. Eventually, classification models (i.e. proposed model, from scratch and pre-trained VGG16 and AlexNet) were trained and validated over the original and augmented datasets to discriminate the jujube into maturity grades.FindingsThe highest success rates reported over the original and augmented datasets were 97.53% (i.e. error of 2.47%) and 99.44% (i.e. error of 0.56%) respectively using Adam optimizer and a learning rate of 0.003.Research limitations/implicationsThe investigation relies upon a single view of the jujube image and the outer appearance of the jujube. In the future, multi-view image capturing system could be employed for the model training/validation.Practical implicationsDue to the vast functional derivatives of jujube, the identification of maturity grades of jujube is paramount in the fruit industry, functional food production industries and pharmaceutical industry. Therefore, the proposed model which is practically feasible and easy to implement could be utilized in such industries.Originality/valueThis research examines the performance of proposed CNN models for selected optimizer and learning rates for the grading of jujube maturity into four classes and compares them with the classical models to depict the sublime model in terms of accuracy, the number of parameters, epochs and computational time. After a thorough investigation of the models, it was discovered that the proposed model transcends both classical models in all aspects for both the original and augmented datasets utilizing Adam optimizer with learning rate of 0.003.

Tuning VGG19 hyperparameters for improved pneumonia classification

This research focuses on the classification of chest X-ray (CXR) images using powerful VGG19 convolutional neural network (CNN)architecture. The classification task involves distinguishing between various chest conditions present in X-ray images, with the aim of assisting medical professionals in achieving accurate and efficient diagnoses. This research work explores the use of the VGG19 model for classifying CXR images using three optimization algorithms: Stochastic gradient descent with momentum (SGDM), root mean square propagation (RMSprop), and adaptive moment estimation (Adam). This study investigates the impact of various factors on hyperparameter adjustments, including a learning rate (LR), mini-batch size (MBS) and training epochs. Additionally, two dropout layers are introduced for weight decay with an L2 factor, and data augmentation techniques are applied with various activation functions. This study not only helps optimize for medical image analysis but also offers valuable insights into the comparative efficacy of popular optimization algorithms in deep learning (DL) applications

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology.

Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.

Optimizing image captioning: The effectiveness of vision transformers and VGG networks for remote sensing

This study presents a comprehensive evaluation of two prominent deep learning models, Vision Transformer (ViT) and VGG16, within the domain of image captioning for remote sensing data. By leveraging the BLEU score, a widely accepted metric for assessing the quality of text generated by machine learning models against a set of reference captions, this research aims to dissect and understand the capabilities and performance nuances of these models across various sample sizes: 25, 50, 75, and 100 samples. Our findings reveal that the Vision Transformer model generally outperforms the VGG16 model across all evaluated sample sizes, achieving its peak performance at 50 samples with a BLEU score of 0.5507. This performance shows that ViT benefits from its ability to capture global dependencies within the data, providing a more nuanced understanding of the images. However, the performance slightly decreases as the sample size increases beyond 50, indicating potential challenges in scalability or overfitting to the training data. Conversely, the VGG16 model shows a different performance trajectory, starting with a lower BLEU score for smaller sample sizes but demonstrating a consistent improvement as the sample size increases, culminating in its highest BLEU score of 0.4783 for 100 samples. This pattern suggests that VGG16 may require a larger dataset to adequately learn and generalize from the data, although it achieves a more modest performance ceiling compared to ViT. Through a detailed analysis of these findings, the study underscores the strengths and limitations of each model in the context of image captioning. The Vision Transformer's superior performance highlights its potential for applications requiring high accuracy in text generation from images. In contrast, the gradual improvement exhibited by VGG16 suggests its utility in scenarios where large datasets are available, and scalability is a priority. This study contributes to the ongoing discourse in the AI community regarding the selection and optimization of deep learning models for complex tasks such as image captioning, offering insights that could guide future research and application development in this field.

Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture.

Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926-0.965) and 0.936 (95%CI: 0.913-0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs.