Visual Geometry Group Research Articles

A Novel Fault Diagnosis Strategy for Diaphragm Pumps Based on Signal Demodulation and PCA-ResNet.

The efficient and accurate identification of diaphragm pump faults is crucial for ensuring smooth system operation and reducing energy consumption. The structure of diaphragm pumps is complex and using traditional fault diagnosis strategies to extract typical fault characteristics is difficult, facing the risk of model overfitting and high diagnostic costs. In response to the shortcomings of traditional methods, this study innovatively combines signal demodulation methods with residual networks (ResNet) to propose an efficient fault diagnosis strategy for diaphragm pumps. By using a demodulation method based on principal component analysis (PCA), the vibration signal demodulation spectrum of the fault condition is obtained, the typical fault characteristics of the diaphragm pump are accurately extracted, and the sample features are enhanced, reducing the cost of fault diagnosis. Afterward, the PCA-ResNet model is applied to the fault diagnosis of diaphragm pumps. A reasonable model structure and advanced residual block design can effectively reduce the risk of model overfitting and improve the accuracy of fault diagnosis. Compared with the visual geometry group (VGG) 16, VGG19, ResNet50, and autoencoder models, the proposed model has improved accuracy by 35.89%, 80.27%, 2.72%, and 6.12%. Simultaneously, it has higher operational efficiency and lower loss rate, solving the problem of diagnostic lag in practical engineering. Finally, a model optimization strategy is proposed through model evaluation metrics and testing. The reasonable parameter range of the model is obtained, providing a reference and guarantee for further optimization of the model.

Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features.

The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%.

A Review Study oftheVisual Geometry Group Approachesfor Image Classification

In the realm of advanced machine learning for image classification, Convolutional Neural Networks (CNNs) stand as a pivotal tool, with the Visual Geometry Group-16(VGG16) model standing out for its emphasis on deepening and expanding CNNsarchitecture to achieve betteraccuracy. However, the complexdesign of VGG16presents challenges regardingcomputational efficiency and scalability. This study addressesthese issues by refining the VGG16architecture through strategic modifications, including reducing convolution blocks, integrating batch normalization (BN) layers, and incorporatinga global average pooling (GAP) layer alongside additional dense and dropout layers.The proposed architecture's effectiveness was assessed through comprehensive experiments across ten benchmark datasets, comparing its performance against the standard VGG16architecture. The proposed architecture sped up the execution time by 63.7% on average across all benchmark datasets, compared to the standard VGG16. Furthermore, the results showed that the proposed architecture outperformed VGG16 by improving the classification accuracy by up to 30.1% based on the overall datasets. In summary, the proposed architectureis made to be compactand accurate. By adjusting parameters, it processes information quickly and accurately. It also includes features to prevent overfitting and improve classification, resulting in a significant advancement in image classification.

Application of deep learning and feature selection technique on external root resorption identification on CBCT images.

Artificial intelligence has been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold: to assess the performance of four deep learning models (DLM) in external root resorption (ERR) identification and to assess the effect of combining feature selection technique (FST) with DLM on their ability in ERR identification. External root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5mm, 1mm, and 2mm). All teeth were scanned using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. Five performance parameters were assessed: classification accuracy, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance. RF + VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF + VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the curve (AUC) of 96%. Kruskal Wallis test revealed a significant difference (p = 0.008) in the prediction accuracy among the eight DLMs. In general, all DLMs have similar performance on ERR identification. However, the performance can be improved by combining FST with DLMs.

Computational Model for the Detection of Diabetic Retinopathy in 2-D Color Fundus Retina Scan

Background:: Diabetic Retinopathy (DR) is a growing problem in Asian countries. DR accounts for 5% to 7% of all blindness in the entire area. In India, the record of DR-affected patients will reach around 79.4 million by 2030. Aims:: The main objective of the investigation is to utilize 2-D colored fundus retina scans to determine if an individual possesses DR or not. In this regard, Engineering-based techniques such as deep learning and neural networks play a methodical role in fighting against this fatal disease. Methods:: In this research work, a Computational Model for detecting DR using Convolutional Neural Network (DRCNN) is proposed. This method contrasts the fundus retina scans of the DR-afflicted eye with the usual human eyes. Using CNN and layers like Conv2D, Pooling, Dense, Flatten, and Dropout, the model aids in comprehending the scan's curve and color-based features. For training and error reduction, the Visual Geometry Group (VGG-16) model and Adaptive Moment Estimation Optimizer are utilized. Results:: The variations in a dataset like 50%, 60%, 70%, 80%, and 90% images are reserved for the training phase, and the rest images are reserved for the testing phase. In the proposed model, the VGG-16 model comprises 138M parameters. The accuracy is achieved maximum rate of 90% when the training dataset is reserved at 80%. The model was validated using other datasets. Conclusion:: The suggested contribution to research determines conclusively whether the provided OCT scan utilizes an effective method for detecting DRaffected individuals within just a few moments.

Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis

Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model’s efficient performance makes it promising for practical implementation in real-world agricultural settings.

Automate facial paralysis detection using vgg architectures

Facial Paralysis (FP) is a debilitating condition that affects individuals worldwide by impairing their ability to control facial muscles and resulting in significant physical and emotional challenges. Precise and prompt identification of FP is crucial for appropriate medical intervention and treatment. With the advancements in deep learning techniques, specifically Convolutional Neural Networks (CNNs), there has been growing interest in utilising these models for automated FP detection. This paper investigates the effectiveness of CNN architectures to identify patients with facial paralysis. The proposed method leveraged the depth and simplicity of Visual Geometry Group (VGG) architectures to capture the intricate relationships within facial images and accurately classify individuals with FP on the YouTube Facial Palsy (YFP) dataset. The dataset consists of 2000 images categorised into individuals with FP and non-injured individuals. Data augmentation techniques were used to improve the robustness and generalisation of the approach proposed. The proposed model consists of a features extraction module utilising the VGG network and a classification module with a Softmax classifier. The performance evaluation metrics include accuracy, recall, precision and F1-score. Experimental results demonstrate that the VGG16 model scored an accuracy of 88.47% with a recall of 83.55%, precision of 92.15% and F1-score of 87.64%. The VGG19 model attained level of precision of 81.95%, with a recall of 72.44%, precision of 88.58% and F1-score of 79.70%. The VGG16 model outperformed the VGG19 model in terms of accuracy, recall, precision, and F1-score. The results indicate that VGG architectures are effective in identifying patients with facial paralysis.

Performance Analysis of Diabetic Retinopathy and Hemorrhages Segmentation System Using Enhanced Visual Geometry Group (EVGG) Architecture

Performance Analysis of Diabetic Retinopathy and Hemorrhages Segmentation System Using Enhanced Visual Geometry Group (EVGG) Architecture

Multi-Modal Information Fusion for Localization of Emergency Vehicles

In urban and city environments, road transportation contributes significantly to the generation of substantial traffic. However, this surge in vehicles leads to complex issues, including hindered emergency vehicle movement due to high density and congestion. Scarcity of human personnel amplifies these challenges. As traffic conditions worsen, the need for automated solutions to manage emergency situations becomes more evident. Intelligent traffic monitoring can identify and prioritize emergency vehicles, potentially saving lives. However, categorizing emergency vehicles through visual analysis faces difficulties such as clutter, occlusions, and traffic variations. Visual-based techniques for vehicle detection rely on clear rear views, but this is problematic in dense traffic. In contrast, audio-based methods are resilient to the Doppler Effect from moving vehicles, but handling diverse background noises remains unexplored. Using acoustics for emergency vehicle localization presents challenges related to sensor range and real-world noise. Addressing these issues, this study introduces a novel solution: combining visual and audio data for enhanced detection and localization of emergency vehicles in road networks. Leveraging this multi-modal approach aims to bolster accuracy and robustness in emergency vehicle management. The proposed methodology consists of several key steps. The presence of an emergency vehicle is initially detected through the preprocessing of visual images, involving the removal of clutter and occlusions via an adaptive background model. Subsequently, a cell-wise classification strategy utilizing a customized Visual Geometry Group Network (VGGNet) deep learning model is employed to determine the presence of emergency vehicles within individual cells. To further reinforce the accuracy of emergency vehicle presence detection, the outcomes from the audio data analysis are integrated. This involves the extraction of spectral features from audio streams, followed by classification utilizing a support vector machine (SVM) model. The fusion of information derived from both visual and audio sources is utilized in the construction of a more comprehensive and refined traffic state map. This augmented map facilitates the effective management of emergency vehicle transit. In empirical evaluations, the proposed solution demonstrates its capability to mitigate challenges like visual clutter, occlusions, and variations in traffic density common issues encountered in traditional visual analysis methods. Notably, the proposed approach achieves an impressive accuracy rate of approximately 98.15% in the localization of emergency vehicles.

Optimized deep learning multi-model investigation of images for ground water level detection

<p>When referring to groundwater in the context of the normal water cycle, the word “reservoir” is acceptable. Compared to the atmosphere or fresh surface water, groundwater has a much greater capacity to hold water. Despite the high degree of variety and complexity of the subsurface ecosystem, there is currently only a small amount of data available. People who depended on reality-based models ran into both of these challenges at some point. Statistical modeling was used in order to get a more accurate calibration of the model throughout the course of time. Because of the expansion in global population, governments in both wealthy and developing nations are increasingly looking to groundwater as an essential resource for satisfying the water requirements of their populations. Because water was preserved in such a large amount, it may be used again, even when there is a drought or other dry time. In this manuscript, a Particle Swarm Optimization (PSO) enabled Visual Geometry Group (VGG) 16 deep learning model is used in order to determine the level of ground water. Accuracy, specificity and sensitivity of PSO enabled VGG is highest among the algorithms used in the experimental study. Accuracy of PSO VGG 16 is 92.5 percent, specificity and sensitivity of PSO VGG 16 is 99 percent.</p>

Brain tumor malignancy classification using improved VGG16 based on MRI images

This study introduces a method based on Magnetic Resonance Imaging (MRI) images and Convolutional Neural Networks (CNN) to categorize the malignancy of brain tumors. The major goal of this research is to develop an accurate and reliable model that aids in tumor early identification, leading to better patient outcomes. Specifically, first, the MRI images undergo preprocessing, which includes intensity normalization and random brightness and contrast adjustments for data augmentation. Next, the Visual Geometry Group (VGG16) model serves as a baseline, enhanced with flatten, dense, and dropout layers. Transfer learning is applied to leverage pre-trained features from VGG16, improving the generalization capability of model. A sizable collection of brain MRI pictures is used to train the model. The experimental outcomes on the MRI dataset illustrate the efficacy of the suggested model. The CNN-based approach achieves high accuracy in classifying brain tumor malignancy based on MRI images, indicating its potential in medical image analysis and its significance in early tumor detection and diagnosis. The proposed model serves as a valuable tool for healthcare professionals, aiding in well-informed decisions and providing an ideal medical approach for early detection of brain tumors. This research opens avenues for future work in medical imaging and deep learning, paving the way for improved treatment and diagnosis of brain tumors.

Comparative analysis of various models for image classification on Cifar-100 dataset

Nowadays, people developed various convolutional neural network (CNN) based models for computer vision. Some famous models, such as GoogLeNet, Residual Network (ResNet), Visual Geometry Group (VGG), and You Only Look Once (YOLO), have different architecture and performances. Determining which model to use may be a troublesome problem for those just starting to study image classification. To solve this problem, we introduce the GoogLeNet, ResNet-18, and VGG-16 models, comparing their architecture, features, and performance. Then we give our suggestions based on the test results to help beginners choose a suitable model. We conducted experiments to train and test GoogLeNet, ResNet-18, and VGG-16 on the Cifar-100 datasets with the same hyperparameters. Based on the test results (test accuracy, average test loss, training loss), we analyze the figures for trends, key points, increase rate, and other features. Then we combine the architecture of each model to make our conclusions. The experimental results show that ResNet-18 can be a good choice when training the model with the Cifar-100 datasets because it performs well after training and has a low time complexity. ResNet-18 also has the fastest convergence speed. GoogLeNet would be the second choice because it functions similarly to ResNet-18 and is even better. However, training GoogLeNet is a time-consuming task. VGG is not recommended in this experiment because it has the worst performance and similar training complexity compared with ResNet-18.

Land scene classification from remote sensing images using improved artificial bee colony optimization algorithm

The images obtained from remote sensing consist of background complexities and similarities among the objects that act as challenge during the classification of land scenes. Land scenes are utilized in various fields such as agriculture, urbanization, and disaster management, to detect the condition of land surfaces and help to identify the suitability of the land surfaces for planting crops, and building construction. The existing methods help in the classification of land scenes through the images obtained from remote sensing technology, but the background complexities and presence of similar objects act as a barricade against providing better results. To overcome these issues, an improved artificial bee colony optimization algorithm with convolutional neural network (IABC-CNN) model is proposed to achieve better results in classifying the land scenes. The images are collected from aerial image dataset (AID), Northwestern Polytechnical University-Remote Sensing Image Scene 45 (NWPU-RESIS45), and University of California Merced (UCM) datasets. IABC effectively selects the best features from the extracted features using visual geometry group-16 (VGG-16). The selected features from the IABC are provided for the classification process using multiclass-support vector machine (MSVM). Results obtained from the proposed IABC-CNN achieves a better classification accuracy of 96.40% with an error rate 3.6%.

A brief review and scientometric analysis on ensemble learning methods for handling COVID-19

Numerous efforts and research have been conducted worldwide to combat the coronavirus disease 2019 (COVID-19) pandemic. In this regard, some researchers have focused on deep and machine-learning approaches to discover more about this disease. There have been many articles on using ensemble learning methods for COVID-19 detection. Still, there seems to be no scientometric analysis or a brief review of these researches. Hence, a combined method of scientometric analysis and brief review was used to study the published articles that employed an ensemble learning approach to detect COVID-19. This research used both methods to overcome their limitations, leading to enhanced and reliable outcomes. The related articles were retrieved from the Scopus database. Then a two-step procedure was employed. A concise review of the collected articles was conducted. Then they underwent scientometric and bibliometric analyses. The findings revealed that convolutional neural network (CNN) is the mostly employed algorithm, while support vector machine (SVM), random forest, Resnet, DenseNet, and visual geometry group (VGG) were also frequently used. Additionally, China has had a significant presence in the numerous top-ranking categories of this field of research. Both study phases yielded valuable results and rankings.

Rapid detection of diabetic retinopathy in retinal images: a new approach using transfer learning and synthetic minority over-sampling technique

The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management.

Deep-CNN based knowledge learning with Beluga Whale optimization using chaogram transformation using intelligent sensors for speech emotion recognition

In recent times, there has been a lot of focus placed on speech emotion recognition, often known as SER. SER is a fundamental approach to emotional human-machine interaction. Utilising DCNNs has resulted in significant advancements being made, notably with regard to the learning of high-level characteristics for SER. However, one of the primary challenges that arises when applying deep neural networks to SER is the problem of overfitting, which occurs commonly when too many parameters are chosen using insufficiently large datasets. The majority of the research that has been done to attempt to solve this problem has focused on translating the audio input into a picture and employing transfer learning methods. According to research, the patterns generated in this region include significant emotional aspects of the speaker. A new speech signal format called Chaogram was designed to project these patterns, which would result in three channels like RGB images, in order to give an input that works with VGG-based Deep-CNNs. The original phase space is then used to reconstruct the voice samples in a three-dimensional space. In the subsequent stage, the Chaogram photos' finer details were accentuated by image enhancing techniques. To learn Chaogram's high-level features and emotion classifications, the Visual Geometry Group (VGG) deep convolutional neural network (DCNN) is employed after being pre-trained on the massive ImageNet dataset using intelligent sensors. We next apply transfer learning to our datasets to further improve the provided model, which we combine with the proposed model. To enhance the hyper-parameter layout of architecture-determined CNNs, a new Deep-CNN with BWO (Beluga Whale Optimization) is introduced. In order to apply Deep–CNN–KL and to the field of SER, thispropose a new method of representing speech signals, which we call Chaograms, by mapping the signal onto a 2D image. On two publicly available emotion datasets, these findings demonstrate the ability of the proposed approach, which has the potential to significantly improve SER applications: EMO-DB and eNTERFACE05. Image enhancement methods that place more emphasis on such features may lead to more precise classification in the performance analysis. Classification accuracy can be considerably improved by adapting these images to work with the pre-trained Deep-CNN inputs.

Electrolytic capacitor surface defect detection based on deep convolution neural network

The existing methods for detecting surface defects in electrolytic capacitors are typically based on conventional machine vision, with limited feature extraction capabilities, poor versatility, slow detection speed, and the inability to achieve accurate and real-time defect detection. In this study, a real-time object detection algorithm based on an improved single shot multibox detector (SSD) is proposed to achieve omnidirectional surface defect detection of electrolytic capacitors. First, an electrolytic capacitor surface image acquisition device was established to capture omnidirectional surface images of the capacitors, and an electrolytic capacitor surface defect dataset was created. Next, the visual geometry group (VGG)-16 network structure was replaced with the MobileNetv2 network structure, effectively reducing the model’s parameter count and improving inference speed. Moreover, the Multibox Loss function was replaced with the Focal Loss function to increase the model’s attention to difficult-to-classify samples and improve model accuracy. Additionally, a transfer learning network model was designed to apply the model to electrolytic capacitors of different colors using small sample learning. Finally, the performance of the improved network model was tested on a dataset of electrolytic capacitor surface defects. The experimental results demonstrate that the parameters quantity of improved model is 3.50 M, the mAP value reaches 92.67 %, which is improved by 2.54 %, and the Macro-F1 value reaches 92.15 %, which is 11.32 % higher than that before improvement. Thus, the proposed improved SSD model provides a theoretical basis and technical prerequisites for automated and intelligent surface defect detection in electrolytic capacitors.

Stability prediction of muddy submarine channel slope based on sub-bottom profile acoustic images and transfer learning

This research addresses the challenging task of predicting the stability of muddy submarine channel slopes, crucial for ensuring safe port operations. Traditional methods falter due to the submerged nature of these channels, impacting navigation and infrastructure maintenance. The proposed approach integrates sub-bottom profile acoustic images and transfer learning to predict slope stability in Lianyungang Port. The study classifies slope stability into four categories: stable, creep, expansion, and unstable based on oscillation amplitude and sound intensity. Utilizing a sub-bottom profiler, acoustic imagery is collected, which is then enhanced through Gabor filtering. This process generates source data to pre-train Visual Geometry Group (VGG)16 neural network. This research further refines the model using targeted data, achieving a 97.92% prediction accuracy. When benchmarked against other models and methods, including VGG19, Inception-v3, Densenet201, Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and an unmodified VGG16, this approach exhibits superior accuracy. This model proves highly effective for real-time analysis of submarine channel slope dynamics, offering a significant advancement in marine safety and operational efficiency.

Cancer staging classification Using VGG16 Architecture Algorithms (TNM staging system)

Understanding the cancer stage aids doctors in developing a prognosis and creating a treatment plan for individual patients. Cancer staging is the process of identifying the amount and location of cancer in the body. It describes the severity of a person's cancer based on the size of the original (primary) tumor as well as the extent to which the cancer has spread in the body. Staging gives medical professionals a standard vocabulary to use when discussing a patient's cancer and working together to choose the best course of action. In recent years, deep learning has emerged as a powerful tool for has been applied to the analysis of pathological images for the detection and classification of diseases. Because deep learning can evaluate enormous volumes of data rapidly and effectively, it is important for the categorization of cancer. This can enhance overall results and assist physicians in making better-informed decisions about patient care. Furthermore, small patterns and traits that might not be visible to the human eye can be trained into deep learning models, enabling earlier identification and more successful therapy. A VGG16 model was developed for cancer classification, and the pre-trained Visual Geometry Group 16 (VGG16) architecture has been applied on convolutional neural network (CNN)-based automatic cancer classification. Some research has studied the use of modified VGG configurations, such as VGG16, to classify cancerous tumors in human tissue images. Studies have shown high efficiency of applying VGG16 in cancer classification, as it was found to be particularly effective in achieving high accuracy rates. The VGG16 model with training was particularly effective in achieving high accuracy rates for cancer classification.

Evolutionary gravitational neocognitron neural network optimized with marine predators optimization algorithm for MRI brain tumor classification

ABSTRACT Magnetic resonance imaging (MRI) is a powerful tool for tumor diagnosis in human brain. Here, the MRI images are considered to detect the brain tumor and classify the regions as meningioma, glioma, pituitary and normal types. Numerous existing methods regarding brain tumor detection were suggested previously, but none of the methods accurately categorizes the brain tumor and consumes more computation period. To address these problems, an Evolutionary Gravitational Neocognitron Neural Network optimized with Marine Predators Algorithm is proposed in this article for MRI Brain Tumor Classification (EGNNN-VGG16-MPA-MRI-BTC). Initially, the brain MRI pictures are collected under Brats MRI image dataset. By using Savitzky-Golay Denoising approach, these images are pre-processed. The features are extracted utilizing visual geometry group network (VGG16). By utilizing VGG16, the features, like Grey level features, Haralick Texture features are extracted. These extracted features are given to EGNNN classifier, which categorizes the brain tumor as glioma, meningioma, pituitary gland and normal. Batch Normalization (BN) layer of EGNNN is eliminated and included with VGG16 layer. Marine Predators Optimization Algorithm (MPA) optimizes the weight parameters of EGNNN. The simulation is activated in MATLAB. Finally, the EGNNN-VGG16-MPA-MRI-BTC method attains 38.98%, 46.74%, 23.27% higher accuracy, 24.24%, 37.82%, 13.92% higher precision, 26.94%, 47.04%, 38.94% higher sensitivity compared with the existing AlexNet-SVM-MRI-BTC, RESNET-SGD-MRI-BTC and MobileNet-V2-MRI-BTC models respectively.