VGG16 Architecture Research Articles

Identification of Potato Plant Pests Using the Convolutional Neural Network VGG16 Method

Pests are one of the main challenges in potato cultivation that can significantly reduce crop yields. Therefore, quick and accurate pest identification is crucial for effective pest control. This research aims to develop a pest identification system for potato plants using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The dataset used consists of images of pests commonly found on potato plants. After the labeling process, these images were used to train the CNN VGG16 model. The research results show that the CNN VGG16 method can identify types of pests with an accuracy rate of 73%. The results serve as a reference to help farmers and agricultural practitioners detect the presence of pests earlier and take the necessary actions to reduce crop losses.

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.

Enhancing Finger Vein Authentication through Deep Learning: A Comparative Study of U-Net and Sequential Models

The use of finger veins as biometric authentication is becoming increasingly popular. However, low-quality finger vein images pose challenges, necessitating innovative approaches for accurate authentication. This research investigates the potential of deep learning techniques in addressing this issue, focusing on two prominent architectures: U-Net and the proposed Sequential Model. The study conducts a comparative analysis of the performance of these models in low-quality finger vein image authentication scenarios. U-Net, known for its image segmentation capabilities, is explored for feature extraction, while the Sequential Model, incorporating a modified VGG16 architecture, brings temporal context through LSTM layers. The research presents an in-depth evaluation of both models based on accuracy, recall, precision, and other relevant metrics. The findings shed light on the suitability of each approach for enhancing the reliability of finger vein authentication in challenging data quality contexts.

Lungs Tumor Classification using Convolutional Neural Network

The research focuses on classifying lung cancer using the VGG-19 architecture. The datasets were sourced from Iraq-Oncology Teaching Hospital with 70% of the data allocated for training and 30% for testing. Performance metrics were computed to evaluate the effectiveness of the classification method. Python is utilized for designing the algorithm and executed using Goggle Colab. The lung tumor classification using VGG-19 offers an accuracy of 95%, sensitivity of 88.79%, specificity of 98.25 %, and F1-Score of 93.28%. However, the low sensitivity value indicates that the VGG-19 architecture is not accurately predicting benign and malignant cases.

Deep ensemble learning for chickenpox detection from clinical images

Chickenpox caused by the varicella-zoster virus is an extremely contagious viral infection common in children and quickly develops into a severe problem. Over 90% of unvaccinated people have been infected. Still, infection occurs at different ages in different parts of the world- Over 70 % of people become infected by the age of 10 years in the United States, the United Kingdom, and Japan, and by the age of 20 in India, West Indies, and South East Asia. Automatic classification of the specific disease is a challenging task to present clinicians to distinguish between different kinds of skin conditions and recommend suitable treatment. Convolutional Neural Networks have recently achieved great success in many machines learning purposes and have presented a state-of-the-art performance in various computer-assisted diagnosis applications. This study proposes a deep neural network-based method that follows an ensemble approach by combining VGG-16, VGG-19, and ResNet-50 architectures to distinguish chickenpox from other skin conditions. Experimental test results have achieved accurate classification with an assuring test accuracy up to 93%.

Detection and Classification on Plant Disease using Deep Learning Techniques

Plant diseases are a major problem for the agriculture industry because they can cause large crop losses and jeopardize food security. Deep learning approaches have demonstrated encouraging results in automating plant disease diagnosis and detection in recent years. In the context of plant disease diagnosis, this study examines the efficacy of two well-known convolutional neural network architectures: DenseNet121 and VGG16. Plant Village datasets are used for pretrained and fine-tuning of the DenseNet121 and VGG16 architectures, respectively. The dataset includes Images of both healthy and sick plants. To guarantee the models' resilience and generalizability, the dataset include 15 different classes and 4 types of plants namely Tomato, Potato and Pepper Bell. We compare the accuracy, precision, recall, and F1-score of DenseNet121 and VGG16 for plant disease classification through extensive testing and analysis. To determine if they are practically feasible for use in real-world applications, we also examine their model complexity and computing efficiency. Our findings show that DenseNet121 and VGG16 can both correctly diagnose plant diseases in a variety of species. Although DenseNet121 outperforms VGG16 in terms of overall accuracy and computational efficiency, both models obtain high accuracy rates. Additionally, DenseNet121 has superior generalization performance, especially in identifying uncommon or underrepresented illness classes. All things considered, this work emphasizes the promise of deep learning models-DenseNet121 in particular-as useful instruments for automated plant disease identification and points to directions for further investigation to improve the efficiency and scalability of such systems for real-world use in agriculture.

Plant Leaf Disease Detection using Deep Learning Algorithms

The Plant Leaf Diseases Detection System addresses the critical challenge of early detection and management of plant diseases, significantly impacting agricultural productivity and food security. Utilizing advanced technologies, this cutting-edge agricultural solution employs a Convolutional Neural Network (CNN) model, specifically based on the VGG19 architecture implemented using Keras. This robust deep learning model is trained on a diverse dataset containing images of both healthy and diseased leaves, allowing it to extract intricate features and accurately classify various plant diseases automatically. The system seamlessly integrates HTML, CSS, and Flask for the front end, while Keras powers the back end, resulting in a user-friendly web application interface. Incorporating this technology not only enhances the efficiency of disease detection but also facilitates user interaction and accessibility

Comparative Study of Keras CNNs for Tuberculosis Detection from Chest X-rays

As a major global health burden, tuberculosis (TB) requires prompt and accurate diagnostic solutions. The effectiveness of Convolutional Neural Networks (CNNs) for TB prediction from chest X-ray images is investigated in this work using a variety of Keras applications. We compare the accuracy, efficiency, and computational resource usage of the VGG16, ResNet50, and EfficientNetV2B2 architectures. By means of comprehensive testing and analysis on a wide range of datasets, we determine the applicability of every model for tuberculosis identification. The findings show that EfficientNetV2B2 is the most promising architecture, achieving remarkable accuracy (99.5%) with enhanced computational efficiency, while VGG16 and ResNet50 show competitive performance. These results highlight how deep learning-based methods have the potential to transform tuberculosis diagnosis by providing doctors with a trustworthy and usable instrument for early detection and treatment. Our research has implications for improving healthcare outcomes and bolstering international efforts to fight tuberculosis. Keywords—EfficientNet, CNN, VGG16,Keras, Accuracy

Comparative Analysis of CNN Architectures for SIBI Image Classification

The classification of images from the Indonesian Sign Language System (SIBI) using VGG16, ResNet50, Inception, Xception, and MobileNetV2 Convolutional Neural Network (CNN) architectures is evaluated in this paper. With Google Colab Pro, a 224 × 224-pixel picture dataset was used for the study. A five-stage technique consisting of Dataset Collection, Dataset Preprocessing, Model Design, Model Training, and Model Testing was applied. Performance evaluation focused on accuracy, precision, recall, and F1-Score. The results identified VGG16 as the top-performing model with an accuracy of 99.60% and an equivalent F1-Score, followed closely by ResNet50 with nearly similar performance. Inception, XCeption, and MobileNetV2 demonstrated balanced performance but with lower accuracy. This study sheds light on the best CNN models to choose for SIBI image classification, and it makes recommendations for further research that include using sophisticated data augmentation methods, investigating novel CNN architectures, and putting the models to practical use.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model’s performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model’s efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

Hierarchical Training of Deep Neural Networks Using Early Exiting.

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication cost, runtime, and privacy concerns. In this study, a novel hierarchical training method for DNNs is proposed that uses early exits in a divided architecture between edge and cloud workers to reduce the communication cost, training runtime, and privacy concerns. The method proposes a brand-new use case for early exits to separate the backward pass of neural networks between the edge and the cloud during the training phase. We address the issues of most available methods that, due to the sequential nature of the training phase, cannot train the levels of hierarchy simultaneously or they do it with the cost of compromising privacy. In contrast, our method can use both edge and cloud workers simultaneously, does not share the raw input data with the cloud, and does not require communication during the backward pass. Several simulations and on-device experiments for different neural network architectures demonstrate the effectiveness of this method. It is shown that the proposed method reduces the training runtime for VGG-16 and ResNet-18 architectures by 29% and 61% in CIFAR-10 classification and by 25% and 81% in Tiny ImageNet classification, respectively, when the communication with the cloud is done over a low bit rate channel. This gain in the runtime is achieved, while the accuracy drop is negligible. This method is advantageous for online learning of high-accuracy DNNs on sensor-holding low-resource devices such as mobile phones or robots as a part of an edge-cloud system, making them more flexible in facing new tasks and classes of data.

Implementing Inception v3, VGG-16 and VGG-19 Architectures of CNN for Medicinal Plant leaves Identification and Disease Detection

Medicinal plants have been a valuable source of healing and healthcare for centuries, serving as a crucial component of traditional medicine systems worldwide. The identification of these plants is a fundamental step in harnessing their therapeutic potential and ensuring their sustainable use. Accurate identification of these plants and the early detection of diseases affecting their leaves are crucial for ensuring a consistent supply of high-quality medicinal resources. In this paper, three types of CNN architectures of deep learning are used to develop robust classification models for distinguishing between healthy and diseased medicinal plant leaves. Inception v3 is known for its versatility in handling various input image sizes without the need for extensive pre-processing, while VGG-19 exhibits high accuracy, robust feature extraction, and suitability for complex image patterns. VGG-16, known for its simplicity and ease of training, provided competitive results, particularly where computational resources were limited. These networks have been pre-trained on large-scale image datasets and fine-tuned using them. These different approaches provide a varied solution to this problem by comparing their accuracy levels, feasibility and to select which one suits the best in this study.

Medical image Classification using Transfer Learning: Convolutional Neural Network Models approach

A relatively low life expectancy in their greatest grade is caused by more debilitating diseases in the medical sector. Any kind of misdiagnosis may lead to improper medical intervention and lower the likelihood that a patient will survive. Making an appropriate treatment plan to cure and enhance the quality of life for patients suffering from any kind of illness starts with an accurate diagnosis. Convolutional neural networks and computer-aided disease detection systems have produced success stories and advanced the science of machine learning significantly. Compared to more conventional neural network layers, the deep convolutional layers automatically extract significant and reliable characteristics from the input space. Within the suggested structure, we use two convolutional neural network architectures (VGG16 and VGG19) to perform two types of medical imaging (x-ray and MRI) investigations in order to categorize brain tumors as normal and up normal as well as classify x-ray pneumonia or normal. The transfer learning strategies by VGG16 and vgg19 models that is, using MRI and x-ray to fine-tune and freeze are then examined in each investigation. In order to increase dataset samples and decrease the likelihood of over-fitting, data augmentation techniques are performed to MRI slices and x-rays for results that can be more broadly utilized. In the proposed studies, the fine-tune VGG19, VGG16 architecture attained highest accuracy up to 0.95with x-ray and 0.80 respectively. The accuracy0.98 used MRI in terms of classification and detection with VGG19.

Identification of Urinary Tract Infection Causing Bacteria from Microbial Colony Images using Deep Learning

Abstract: Diagnosis of microbiological and bacterial infections involves extensive procedures ofsample culture and microscopic examination. The diagnosis process begins with the identification of symptoms followed by collection of test samples in the form of blood,scraps of skin lesions, mucus, sputum, urine etc. The entire process of bacteria routine culture and bacteriological examination may take as long as 10 to 11 days. Due to the long time required for the standard process of species identification, high costs and need of human expertise it is beneficial to use methods that do not rely on conventional methods. In this work we propose a comprehensive Deep Learning basedapproach to identify urinary Tract Infection (UTI) causing bacteria from microbial colony images of bacteria cultured on agar plates. The approach explains the use of VGG-19 and Inception-v3 deep learning architectures, in bacteria identification evaluated on The Annotated Germs for Automated Recognition (AGAR) dataset, an image dataset of microbial colonies cultured on agar plates. The findings affirmed the significant potential of employing deep learning techniques for the identification of microbial colonies and their classification using Petri dish images.

Analysis of Logistic Regression Regularization in Wild Elephant Classification with VGG-16 Feature Extraction

The research article explores the intersection of image-based wildlife classification and logistic regression regularization, focusing on the classification of wild elephant species. It begins by highlighting the significance of ecological research in biodiversity monitoring and conservation and introduces Convolutional Neural Networks (CNNs) as potent tools for feature extraction from images. The VGG-16 model is particularly emphasized for its ability to capture hierarchical representations of visual features crucial for classification tasks. The integration of VGG-16 feature extraction with logistic regression regularization is proposed as a compelling approach, offering a balance between sophisticated feature representation and efficient classification algorithms. The literature review delves into image-based wildlife classification, emphasizing the role of CNNs, especially VGG-16, in extracting discriminative features. It discusses the fusion of VGG-16 features with logistic regression and the challenges in this field, such as dataset annotation and environmental variability. The method section outlines the dataset acquisition, feature extraction using the VGG-16 architecture, and model configuration using logistic regression with lasso and ridge regularization. The process of finding the optimal regularization parameter (lambda) and model evaluation through cross-validation is detailed. Results showcase the optimal lambda values for lasso and ridge regularization and compare the performance of logistic lasso and logistic ridge models. Misclassification analysis reveals factors influencing classification accuracy, including feature variability and contextual complexity. The discussion reflects on the implications of the findings, emphasizing the importance of lambda selection and addressing challenges in wildlife classification. It suggests avenues for further research, such as advanced modeling techniques and feature engineering approaches. In conclusion, the study contributes to advancing wildlife classification efforts by leveraging state-of-the-art techniques and sheds light on opportunities to enhance classification accuracy in wildlife conservation.

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.

Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the "ImageNet" collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model's generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model's outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% F1-score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes.

Strawberry disease detection using transfer learning of deep convolutional neural networks

The impact of disease on strawberry quality and yield holds considerable significance, prompting researchers to explore effective methodologies for disease detection in strawberries. Among these, deep learning has emerged as a pivotal approach. In this regard, this research explored the utilization of transfer learning in deep convolutional neural networks (CNNs) to identify various strawberry diseases. Specifically, we utilized models pre-trained on the ImageNet dataset, namely VGG19, Inception V3, ResNet50, and DenseNet121 architectures, employing both fine-tuning and feature extraction techniques of transfer learning and consequently compared to the models without transfer learning. The target diseases for identification included angular leaf spot, anthracnose, gray mold, and powdery mildew on both fruit and leaves. The study outcomes revealed that Resnet-50 consistently achieved the highest accuracy across all three configurations, achieving its peak accuracy at 94.4 %, followed by Densenet-121 with an accuracy of 94.1 % attained through fine-tuning. These results highlighted the superior performance of fine-tuned models over using these models solely as feature extractors for identifying strawberry diseases. Furthermore, this study revealed that the application of transfer learning substantially reduced training time and resulted in a lower count of trainable parameters than models trained without transfer learning. These outcomes strongly endorse the practicality and effectiveness of employing transfer learning techniques for precise strawberry disease identification. Additionally, further research can explore the application of transfer learning to a broader range of crops and diseases, potentially enhancing agricultural disease detection methodologies.

Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier

The cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture.

Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two Major Computed Tomography Patterns.

Interstitial lung diseases (ILD) encompass various disorders characterized by inflammation and/or fibrosis in the lung interstitium. These conditions produce distinct patterns in High-Resolution Computed Tomography (HRCT). We employ a deep learning method to diagnose the most commonly encountered patterns in ILD differentially. Patients were categorized into usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and normal lung parenchyma groups. VGG16 and VGG19 deep learning architectures were utilized. 85% of each pattern was used as training data for the artificial intelligence model. The models were then tasked with diagnosing the patterns in the test dataset without human intervention. Accuracy rates were calculated for both models. 1 The success of the VGG16 model in the test phase was 95.02% accuracy. 2 Using the same data, 98.05% accuracy results were obtained in the test phase of the VGG19 model. Deep Learning models showed high accuracy in distinguishing the two most common patterns of ILD.

Forward layer-wise learning of convolutional neural networks through separation index maximizing

This paper proposes a forward layer-wise learning algorithm for CNNs in classification problems. The algorithm utilizes the Separation Index (SI) as a supervised complexity measure to evaluate and train each layer in a forward manner. The proposed method explains that gradually increasing the SI through layers reduces the input data’s uncertainties and disturbances, achieving a better feature space representation. Hence, by approximating the SI with a variant of local triplet loss at each layer, a gradient-based learning algorithm is suggested to maximize it. Inspired by the NGRAD (Neural Gradient Representation by Activity Differences) hypothesis, the proposed algorithm operates in a forward manner without explicit error information from the last layer. The algorithm’s performance is evaluated on image classification tasks using VGG16, VGG19, AlexNet, and LeNet architectures with CIFAR-10, CIFAR-100, Raabin-WBC, and Fashion-MNIST datasets. Additionally, the experiments are applied to text classification tasks using the DBPedia and AG’s News datasets. The results demonstrate that the proposed layer-wise learning algorithm outperforms state-of-the-art methods in accuracy and time complexity.