VGG16 Convolutional Neural Network 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.

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

Detection of bacterial spot disease on tomato leaves using a Convolutional Neural Network (CNN)

Tomato diseases have become a major concern to the tomato production sector around the world. A huge proportion of tomato crops deteriorate yearly during growth or after harvesting due to the infections caused by fungus, viruses and bacteria. Early detection of these diseases plays a crucial role in alleviating overall production loss. Over the past decades, farmers have been using visual observation to identify a crop disease in a field. However, the visual observation method is labour intensive, time consuming, and prone to human error. Currently, intelligent approaches have been widely used to detect and classify these diseases. The objective of this study is to design a convolutional neural network VGG16 net architecture that is able to detect tomato diseases on tomato leaves. A model that can successfully detect 5 tomato leaf diseases was developed: (1) Bacterial spot, (2) Early blight, (3) Late blight, (4) Septoria leaf and (5) Yellow leaf curl with a training accuracy of 0.9328.

PLANT DISEASE RECOGNITION USING VGG-16

Agriculture and modern farming is one of the fields where IoT and automation can have a great impact. Maintaining healthy plants and monitoring their environment in order to identify or detect diseases is essential in order to maintain a maximum crop yield. The implementation of current high rocketing technologies including artificial intelligence (AI), machine learning, and deep learning has proved to be extremely important in modern agriculture as a method of advanced image analysis domain. Artificial intelligence adds time efficiency and the possibility of identifying plant diseases, in addition to monitoring and controlling the environmental conditions in farms. Several studies showed that machine learning and deep learning technologies can detect plant diseases upon analyzing plant leaves with great accuracy and sensitivity. In this study, considering the worth of machine learning for disease detection, we present a convolutional neural network VGG-16 model to detect plant diseases, to allow farmers to make timely actions with respect to treatment without further delay. To carry this out, 19 different classes of plants diseases were chosen, where 15,915 plant leaf images (both diseased and healthy leaves) were acquired from the Plant Village dataset for training and testing. Based on the experimental results, the proposed model is able to achieve an accuracy of about 95.2% with the testing loss being only 0.4418. The proposed model provides a clear direction toward a deep learning-based plant disease detection to apply on a large scale in future. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming.

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.

Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding.

Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications. This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering. The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%. We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.

Deep Learning Based Tomato Leaf Disease Detection for Smart Farming

Tomato cultivation has a major role in global agriculture, contributing to both food security and economic stability. Still, tomato plants are susceptive to various diseases, which can specially reduce yield and quality. Plant disease early detection and treatment depend more and more on automatic disease identification and categorization technologies. In this paper, we present a Deep Learning based approach for tomato leaf diseases classification using VGG16 Convolutional Neural Network architecture. The VGG16 model is well-known for its superior picture categorization ability, A data set is used to train the VGG16 that includes healthy tomato leaves and leaves that are contaminated with common diseases like Early Blight, Late Blight, Leaf Mold, and Bacterial Spot. The dataset used in training and assessment comprises a varied assortment of high-resolution images acquired from various sources, guaranteeing the resilience and adaptability of the suggested model. Our utilization of data augmentation methods aims to improve the model's capacity to address variations in leaf characteristics, such as alterations in lighting, background complexity, and leaf positioning. Through experimental findings, we have validated the efficacy of our approach in precisely categorizing tomato leaf diseases with remarkable recall rates and precision. Comparative evaluations against leading techniques highlight the superior classification performance and computational efficiency of the VGG16-based model.

BRAIN NEOPLASM IDENTIFICATION USING DEEP LEARNING

This study offers a powerful brain tumor detection system that makes use of deep learning techniques, especially the VGG16 learning algorithm and Convolutional Neural Network techniques (CNN). The ultimate objective of the recommended strategy is to enhance the accuracy and efficiency of identifying tumors in magnetic resonance imaging (MRI) images.. Utilizing CNN allows the model to automatically extract intricate features from input images, while VGG16, renowned for its deep architecture, contributes to a more intricate understanding of complex patterns. The integration of these algorithms empowers the system to discern subtle nuances indicative of brain tumors, providing a reliable and swift diagnostic tool for medical practitioners. Experimental results demonstrate the system's effectiveness in achieving high accuracy rates, showcasing its potential as an invaluable asset in early tumor detection and subsequent medical intervention. KEYWORDS: CNN, Brain tumor, Machine learning, Medical imaging

Automatic detection of potholes using VGG-16 pre-trained network and Convolutional Neural Network

A self-driving car is necessary to implement traffic intelligence because it can vastly enhance both the safety of driving and the comfort of the driver by adjusting to the circumstances of the road ahead. Road hazards such as potholes can be a big challenge for autonomous vehicles, increasing the risk of crashes and vehicle damage. Real-time identification of road potholes is required to solve this issue. To this end, various approaches have been tried, including notifying the appropriate authorities, utilizing vibration-based sensors, and engaging in three-dimensional laser imaging. Unfortunately, these approaches have several drawbacks, such as large initial expenditures and the possibility of being discovered. Transfer learning is considered a potential answer to the pressing necessity of automating the process of pothole identification. A Convolutional Neural Network (CNN) is constructed to categorize potholes effectively using the VGG-16 pre-trained model as a transfer learning model throughout the training process. A Super-Resolution Generative Adversarial Network (SRGAN) is suggested to enhance the image's overall quality. Experiments conducted with the suggested approach of classifying road potholes revealed a high accuracy rate of 97.3%, and its effectiveness was tested using various criteria. The developed transfer learning technique obtained the best accuracy rate compared to many other deep learning algorithms.

Ensemble Deep Learning Models for Heart Failure Prediction from Real Healthcare Records

Cardiovascular diseases (CVDs) remain the leading cause of death globally, necessitating advancements in non-invasive and efficient monitoring techniques. "Retinabeat" proposes a novel approach to CVD monitoring by leveraging the diagnostic potential of retinal vascular patterns. Utilizing state-of-the-art retinal imaging combined with the VGG19 convolutional neural network, this system aims to detect and analyze subtle changes in the retinal vasculature that correlate with cardiovascular health. Preliminary studies suggest that these retinal indicators can serve as reliable markers of cardiovascular anomalies, providing a non-invasive, cost-effective, and readily accessible method for early detection and ongoing management of heart health. This paper details the development and validation of the Retinabeat system, evaluates its performance against traditional methods, and discusses the implications of integrating advanced image processing technologies in routine cardiovascular care. The outcomes highlight the system’s potential to enhance predictive diagnostics and personalize health monitoring, paving the way for broader applications in telemedicine and preventive healthcare. Keywords— Cardiovascular diseases, VGG19, telemedicine, Retinal images

Lung Cancer Prediction and Classification Using Decision Tree and VGG16 Convolutional Neural Networks

Introduction A malignant abnormal growth that starts in the tissues of the lungs is called Lung Cancer. It ranks among the most common and lethal cancers globally. Lung Cancer is particularly dangerous because of its aggressive nature and how quickly it can extend to other areas of the body. We propose a two-step verification architecture to check the presence of Lung Cancer. The model proposed by this paper first assesses the patient based on a few questions about the patient's symptoms and medical background. Then, the algorithm determines whether the patient has a low, medium, or high risk of developing lung cancer by diagnosing the response using the “Decision Tree” classification at an accuracy of 99.67%. If the patient has a medium or high risk, we further validate the finding by examining the patient's CT scan image using the “VGG16” CNN model at an accuracy of 92.53%. Background One of the key areas of research on Lung Cancer prediction is to identify patients based on symptoms and medical history. Its subjective nature makes it challenging to apply in real-world scenarios. Another research area in this field involves forecasting the presence of cancer cells using CT scan imagery, providing high accuracy. However, it requires physician intervention and is not appropriate for early-stage prediction. Objective This research aims to forecast the severity of Lung Cancer by analyzing the patient with a few questions regarding the symptoms and past medical conditions. If the patient has a medium or a high risk, we further examine their CT scan, validate the result and also predict the type of Lung Cancer. Methodology This paper uses the “Decision Tree” algorithm and the Customised “VGG16” model of CNN for the implementation. The “Decision Tree” algorithm is used to analyze the answers given by the patient to distinguish the severity of Lung Cancer. We further use Convolution Neural Networks with a Customised “VGG16” model to examine the patient's CT scan image, validate the result and categorize the type of Lung Cancer. Results The “Decision Tree” approach for forecasting the severity of lung cancer yields an accuracy of 99.67%. The accuracy of the customized “VGG16” CNN model to indicate the type of Lung Cancer suffered by the patient is 92.53% Conclusion This research indicates that our technique provides greater accuracy than the prior approaches for this problem and has extensive use in the prognosis of Lung Cancer.

Advancing Crack Detection Using Deep Learning Solutions for Automated Inspection of Metallic Surfaces

The deep Convolutional Neural Network (CNN) architecture used in this research study provides a proof of concept for crack detection on the metallic surface of a hex nut. The goal is to create an automated receiving inspection process to supplement human inspections conducted on-site. Conventional image processing techniques (IPTs) have been extensively used for mechanical infrastructure fault detection. These techniques focus on image modification to extract typical features, such as surface fractures in materials like steel and concrete. However, obstacles presented by a variety of real-world variables, such as changes in lighting and shadows, make it difficult to use IPTs. Our suggested vision-based method employs a deep learning CNN to overcome these difficulties, eliminating the need to explicitly compare fault features. CNNs are more resilient to shifting real-world situations than IPTs since they are naturally trained to identify characteristics in images. Following training on a dataset of 1081 images with dimensions of 256 x 256 pixels, the VGG16 CNN architecture achieved an impressive accuracy of around 94.17%. Additional CNN architectures, including ResNet, MobileNet, AlexNet, and LeNet-5, are employed to assess and compare fault detection accuracies in order to select the most appropriate architecture for the model. To evaluate the robustness and flexibility of the suggested method in various situations, we conducted tests with 206 images from an alternative structure that was not part of the training dataset. These images depicted a range of circumstances, such as intense light patches and tiny fissures. The outcomes showed that our proposed method outperforms current approaches, highlighting its usefulness in practical situations involving the identification of metallic defects.

Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs

BackgroundHip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity.MethodsWe collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort. The radiographs were split in half to create hip radiographs (n = 1022). One orthopaedic surgeon and one resident assessed the radiographs for hip dysplasia on either side. We used the center edge (CE) angle as the primary diagnostic criteria. Hips with a CE angle < 20°, 20° to 25°, and > 25° were labeled as dysplastic, borderline, and normal, respectively. The dysplastic hips were also classified with both Crowe and Hartofilakidis classification of dysplasia. The dataset was divided into train, validation, and test subsets using 80:10:10 split-ratio that were used to train two deep learning models to classify images into normal, borderline and (1) Crowe grade 1–4 or (2) Hartofilakidis grade 1–3. A pre-trained on Imagenet VGG16 convolutional neural network (CNN) was utilized by performing layer-wise fine-turning.ResultsBoth models struggled with distinguishing between normal and borderline hips. However, achieved high accuracy (Model 1: 92.2% and Model 2: 83.3%) in distinguishing between normal/borderline vs. dysplastic hips. The overall accuracy of Model 1 was 68% and for Model 2 73.5%. Most misclassifications for the Crowe and Hartofilakidis classifications were +/- 1 class from the correct class.ConclusionsThis pilot study shows promising results that a deep learning model distinguish between normal and dysplastic hips with high accuracy. Future research and external validation are warranted regarding the ability of deep learning models to perform complex tasks such as identifying and classifying disorders using plain radiographs.Level of EvidenceDiagnostic level IV

Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16

In the context of agricultural sustainability and food security, timely and precise identification of plant diseases holds paramount significance. This research proposes an innovative approach to diagnose bacterial leaf blight (BLB), rice blast (RB), and brown spot (BS) in rice leaves utilizing the VGG16 convolutional neural network (CNN). By leveraging deep learning capabilities, the proposed model analyzes high-resolution images of rice leaves to classify and distinguish between the different diseases. The VGG16 architecture has shown remarkable performance in image recognition tasks, and this study aims to harness its potential for precision agriculture. Rice (Oryza sativa) is a staple crop that plays a crucial role in global food security. Timely and precise diagnosis of these diseases is vital for effective disease management and crop protection. Our methodology involves the collection and curation of a diverse dataset of rice leaf images. The VGG16 model, a proven deep convolutional neural network (CNN) architecture, is employed for extraction of features and classification. The results of our study demonstrate the efficacy of the VGG16 model in distinguishing between different rice leaf diseases with remarkable precision. This high level of accuracy holds great promise for the field of agriculture, offering a non-invasive and efficient means of disease detection. Early disease identification can facilitate timely intervention, reducing crop losses and mitigating the economic impact on farmers. The classification accuracy of the proposed method is 97.77% using the publicly available dataset.

Temporal Fusion of Time-Distributed VGG-16 and LSTM for Precise Action Recognition in Video Sequences

Autonomous systems play a vital role in creating an ambient environment, but for comprehensive human-robot interaction, the system must interpret and identify specific human behaviours. The demand for recognizing human activity from videos has significantly increased across diverse domains, such as medicine, intelligent video surveillance, human-robot interaction, and ambient-assisted environments. Deep learning has emerged as a pioneering technique for human activity recognition from videos due to its ability to learn features from input videos automatically. This research proposes a novel approach for recognizing activities in videos by leveraging the combined power of the VGG-16 convolutional neural network (CNN) and the Long Short-Term Memory (LSTM). To assess the efficiency of our suggested methodology, experiments are conducted on the widely used benchmark dataset UCF-50. The experimental results demonstrate that our VGG-16 and LSTM-based model outperforms state-of-the-art methods for activity recognition, achieving higher accuracy and robustness across various activity categories.

Key generation method from fingerprint image based on deep convolutional neural network model

Biometrics effect our live. Security applications employ biometrics. Biometric encryption is growing. Encryption requires biometric key creation. Long, random, and unexpected is the key. Information and communication security research emphasizes long, strong encryption keys. The proposed system uses fingerprint biometrics to generate a long, random biometric encryption key for symmetric encryption. Pre-processing removed noise from donor fingerprint images in the dataset. The program then trains an updateable Tuned VGG-16 convolutional neural network model and tests it on fingerprint images to learn fundamental fingerprint properties. The convolutional neural netwoprk CNN model retains the final weights for the second model to extract encryption key features. Transfer learning built a second convolutional neural network model to retrieve features without relearning. Keeping vector mean for processing. The last step generates an encryption key based on each person's vector of unique biometric features can be used for symmetric encryption algorithms to encrypt personal documents on the personal PC or personal cloud. Our CNN based method uses biometrics to recognize people and create safe and trustworthy encryption keys with over 99% accuracy in testing. Our 98%-accurate deep ANN classifier exceeds the support vector machine and random forest classifiers.

Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks.

The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures.

Ethnicity Classification: Discriminating Between Iranian and Asian Populations Using Hybrid Deep Learning Algorithm

The rapid advancement of deep learning techniques has opened new avenues for solving complex classification problems in various domains. This research explores the application of Convolutional Neural Networks (CNN), specifically the VGG16 architecture, in conjunction with Support Vector Machines (SVM) for the classification of ethnicity using Iranian and Asian facial images. The classification of ethnicity based on facial features is a challenging task due to the subtle and complex variations within and between different ethnic groups. The proposed methodology involves a two-step process. First, the VGG16 CNN model is utilized to extract high-level features from the facial images. The pre-trained VGG16 model, known for its depth and representational power, is fine-tuned on the dataset to capture relevant ethnic features. The extracted features are then fed into an SVM classifier, which is trained to distinguish between Iranian and Asian facial characteristics. A comprehensive dataset consisting of labeled Iranian and Asian facial images is compiled and preprocessed for training and evaluation. The model's performance is assessed using metrics such as accuracy. Various experiments are conducted to optimize hyperparameters and validate the generalization capability of the proposed model. Additionally, visualization techniques are employed to provide insights into the features learned by the CNN and the decision boundaries established by the SVM. The results indicate that the combined approach of CNN-VGG16 and SVM yields promising accuracy in the classification of ethnicity based on facial images.

Roadmap for Digital Image Forgery Detection Using Deep Learning

Technology advances are prominent today while influencing all aspects of our lives. Misuse of information has also increased as a result of technical improvements. As a result, investigators have the enormous task of recognizing modified information and distinguishing this from genuine data. Among the most prevalent techniques for electronic image alteration is splicing, which includes replicating a specific section using the same or different photograph and transferring it to a new image. In the wake of this issue, picture identification of forgeries has arisen as a viable approach for confirming the validity of online photographs.&#x0D; Within this paper, we propose a strategy depending on the cutting-edge ResNet50v2 neural networks framework. This study describes an approach intended specifically for detecting splicing, among the most common forms of online picture forgeries. The VGG-16 convolutional neural network model is used in our technique. The recommended network topology accepts picture patchwork as data and generates identification outcomes on the patch, categorizing them as legitimate or fraudulent. We select pieces from the primary picture areas and the inserted splicing boundaries throughout the teaching stage. This paper proposes an effective technique for identifying splices in electronic photos, proving the usefulness of our deep learning-powered strategy and emphasizing its superior results over previous alternatives.

Gender identification of the horsehair crab, Erimacrus isenbeckii (Brandt, 1848), by image recognition with a deep neural network

Appearance-based gender identification of the horsehair crab [Erimacrus isenbeckii (Brandt, 1848)] is important for preventing indiscriminate fishing of female crabs. Although their gender is easily identified by visual observation of their abdomen because of a difference in the forms of their sex organs, most of the crabs settle with their shell side upward when placed on a floor, making visual gender identification difficult. Our objective is to use deep learning to identify the gender of the horsehair crab on the basis of images of their shell and abdomen sides. Deep learning was applied to a photograph of 60 males and 60 females captured in Funka Bay, Southern Hokkaido, Japan. The deep learning algorithms used the AlexNet, VGG-16, and ResNet-50 convolutional neural networks. The VGG-16 network achieved high accuracy. Heatmaps were enhanced near the forms of the sex organs in the abdomen side (F-1 measure: 98%). The bottom of the shell was enhanced in the heatmap of a male; by contrast, the upper part of the shell was enhanced in the heatmap of a female (F-1 measure: 95%). The image recognition of the shell side based on a deep learning algorithm enabled more precise gender identification than could be achieved by human-eye inspection.