Search Results (1,569)

Background/Objectives: The accurate categorization of brain MRI images into tumor and non-tumor categories is essential for a prompt and effective diagnosis. This paper presents a novel methodology utilizing advanced Convolutional Neural Network (CNN) designs to tackle the complexity and unpredictability present in brain MRI data. Methods: The methodology commences with an extensive preparation phase that includes image resizing, grayscale conversion, Gaussian blurring, and the delineation of the brain region for preparing the MRI images for analysis. The Multi-verse Optimizer (MVO) is utilized to optimize data augmentation parameters and refine the configuration of trainable layers in VGG16 and ResNet50. The model’s generalization capabilities are significantly improved by the MVO’s ability to effectively balance computational cost and performance. Results: The amalgamation of VGG16 and ResNet50, further refined by the MVO, exhibits substantial enhancements in classification metrics. The MVO-optimized hybrid model demonstrates enhanced performance, exhibiting a well-calibrated balance between precision and recall, rendering it exceptionally trustworthy for medical diagnostic applications. Conclusions: The results highlight the effectiveness of MVO-optimized CNN models for classifying brain tumors in MRI data. Future investigations may examine the model’s applicability to multiclass issues and its validation in practical clinical environments. Full article

Image security faces increasing challenges with the widespread application of computer science and artificial intelligence. Although chaotic systems are employed to encrypt images and prevent unauthorized access or tampering, the degradation that occurs during the binarization process in chaotic systems reduces security. The chaos- and DNA-based image encryption schemes increases its complexity, while the integration of deep learning with image encryption is still in its infancy and has several shortcomings. An image encryption scheme with high security and efficiency is required for the protection of the image. To address these problems, we propose a novel image encryption scheme based on the lightweight VGG (LVGG), referred to as LVGG-IE. In this work, we design an LVGG network with fewer layers while maintaining a high capacity for feature capture. This network is used to generate a key seed, which is then employed to transform the plaintext image into part of the initial value of a chaotic system, ensuring that the chaos-based key generator correlates with the plaintext image. A dynamic substitution box (S-box) is also designed and used to scramble the randomly shuffled plaintext image. Additionally, a single-connected (SC) layer is combined with a convolution layer from VGG to encrypt the image, where the SC layer is dynamically constructed by the secret key and the convolution kernel is set to . The encryption efficiency is simulated, and the security is analyzed. The results show that the correlation coefficient between adjacent pixels in the proposed scheme achieves . The NPCR exceeds 0.9958, and the UACI falls within the theoretical value with a significance level of 0.05. The encryption quality, the security of the dynamic S-box and the SC layer, and the efficiency are tested. The result shows that the proposed image encryption scheme demonstrates high security, efficiency, and robustness, making it effective for image security in various applications. Full article

The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid reliability and consumers’ appliances in a smart city environment. Conventionally, power quality monitoring relies on trivial machine learning classifiers or signal processing methods. However, recent advancements have introduced Deep Convolution Neural Networks (DCNNs) as promising methods for the detection and classification of PQDs. These techniques have the potential to demonstrate high classification accuracy, making them a more appropriate choice for real-time operations in a smart city framework. This paper presents a voting ensemble approach to classify sixteen PQDs, using the DCNN architecture through transfer learning. In this process, continuous wavelet transform (CWT) is employed to convert one-dimensional (1-D) PQD signals into time–frequency images. Four pre-trained DCNN architectures, i.e., Residual Network-50 (ResNet-50), Visual Geometry Group-16 (VGG-16), AlexNet and SqeezeNet are trained and implemented in MATLAB, using images of four datasets, i.e., without noise, 20 dB noise, 30 dB noise and random noise. Additionally, we also tested the performance of ResNet-50 with a squeeze-and-excitation (SE) mechanism. It was observed that ResNet-50 with the SE mechanism has a better classification accuracy; however, it causes computational overheads. The classification performance is enhanced by using the voting ensemble model. The results indicate that the proposed scheme improved the accuracy (99.98%), precision (99.97%), recall (99.80%) and F1-score (99.85%). As an outcome of this work, it is demonstrated that ResNet-50 with the SE mechanism is a viable choice as a single classification model, while an ensemble approach further increases the generalized performance for PQD classification. Full article

This study facilitates the extraction of foveal avascular zone (FAZ) metrics from optical coherence tomography angiography (OCTA) images, offering valuable clinical insights and enabling detailed statistical analysis of FAZ size and shape across three patient groups: healthy, type II diabetes mellitus and both type II diabetes mellitus (DM) and high blood pressure (HBP). Additionally, it evaluates the performance of four deep learning (DL) models—U-Net, U-Net with DenseNet121, U-Net with MobileNetV2 and U-Net with VGG16—in automating the segmentation of the FAZ. Manual segmentation of the images by ophthalmological clinicians was performed initially, and data augmentation was used to enhance the dataset for robust model training and evaluation. Consequently, the original set of 103 full retina OCTA images was extended to 672 cases, including 42 images from normal patients, 357 images from DM patients, and 273 images from patients with both DM and HBP. Among the models, U-Net with DenseNet outperformed the others, achieving the highest accuracy, Intersection over Union (IoU), and Dice coefficient across all patient groups. This research is distinct in its focus on full retina OCTA images and its inclusion of patients with both hypertension and diabetes, an area that is less studied in the existing literature. Full article

Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life of modules is also increasing. Regular maintenance and inspection are vital to extend the lifespan of these systems, minimize energy losses, and protect the environment. This paper presents an innovative explainable AI model for detecting anomalies in solar photovoltaic panels using an enhanced convolutional neural network (CNN) and the VGG16 architecture. The model effectively identifies physical and electrical changes, such as dust and bird droppings, and is implemented using the PyQt5 Python tool to create a user-friendly interface that facilitates decision-making for users. Key processes included dataset balancing through oversampling and data augmentation to expand the dataset. The model achieved impressive performance metrics: 91.46% accuracy, 98.29% specificity, and an F1 score of 91.67%. Overall, it enhances power generation efficiency and prolongs the lifespan of photovoltaic systems, while minimizing environmental risks. Full article

Background: Lung cancer remains a significant health concern, and the effectiveness of early detection significantly enhances patient survival rates. Identifying lung tumors with high precision is a challenge due to the complex nature of tumor structures and the surrounding lung tissues. Methods: To address these hurdles, this paper presents an innovative three-step approach that leverages Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and VGG16 algorithms for the accurate reconstruction of three-dimensional (3D) lung tumor images. The first challenge we address is the accurate segmentation of lung tissues from CT images, a task complicated by the overwhelming presence of non-lung pixels, which can lead to classifier imbalance. Our solution employs a GAN model trained with a reinforcement learning (RL)-based algorithm to mitigate this imbalance and enhance segmentation accuracy. The second challenge involves precisely detecting tumors within the segmented lung regions. We introduce a second GAN model with a novel loss function that significantly improves tumor detection accuracy. Following successful segmentation and tumor detection, the VGG16 algorithm is utilized for feature extraction, preparing the data for the final 3D reconstruction. These features are then processed through an LSTM network and converted into a format suitable for the reconstructive GAN. This GAN, equipped with dilated convolution layers in its discriminator, captures extensive contextual information, enabling the accurate reconstruction of the tumor’s 3D structure. Results: The effectiveness of our method is demonstrated through rigorous evaluation against established techniques using the LIDC-IDRI dataset and standard performance metrics, showcasing its superior performance and potential for enhancing early lung cancer detection. Conclusions:This study highlights the benefits of combining GANs, LSTM, and VGG16 into a unified framework. This approach significantly improves the accuracy of detecting and reconstructing lung tumors, promising to enhance diagnostic methods and patient results in lung cancer treatment. Full article

The rapid and accurate detection of fire scenes in various environments is crucial for effective disaster management and mitigation. Fire scene classification is a critical aspect of modern fire detection systems that directly affects public safety and property preservation. This research introduced a novel hybrid deep learning model designed to enhance the accuracy and efficiency of fire scene classification across diverse environments. The proposed model integrates advanced convolutional neural networks with multiscale feature extraction, attention mechanisms, and ensemble learning to achieve superior performance in real-time fire detection. By leveraging the strengths of pre-trained networks such as ResNet50, VGG16, and EfficientNet-B3, the model captures detailed features at multiple scales, ensuring robust detection capabilities. Including spatial and channel attention mechanisms further refines the focus on critical areas within the input images, reducing false positives and improving detection precision. Extensive experiments on a comprehensive dataset encompassing wildfires, building fires, vehicle fires, and non-fire scenes demonstrate that the proposed framework outperforms existing cutting-edge techniques. The model also exhibited reduced computational complexity and enhanced inference speed, making it suitable for deployment in real-time applications on various hardware platforms. This study sets a new benchmark for fire detection and offers a powerful tool for early warning systems and emergency response initiatives. Full article

To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles. Firstly, a structure-from-motion (SFM) algorithm generates a 3D point cloud from photographs. The feature detection methods scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE are compared across six datasets, with SIFT proving the most effective (matching rate higher than 0.12). Using K-nearest-neighbor matching and random sample consensus (RANSAC), refined feature point matching and 3D spatial representation are achieved via antipodal geometry. Then, the Poisson surface reconstruction algorithm converts the point cloud into a mesh model. Additionally, texture images are enhanced by leveraging a visual geometry group (VGG) network-based deep learning approach. Content images from a dataset provide geometric contours via higher-level VGG layers, while textures from style images are extracted using the lower-level layers. These are fused to create texture-transferred images, where the image quality assessment (IQA) metrics SSIM and PSNR are used to evaluate texture-enhanced images. Finally, texture mapping integrates the enhanced textures with the mesh model, improving the scene representation with enhanced texture. The method presented in this paper surpassed a LiDAR-based reconstruction approach by $20\%$ in terms of point cloud density and number of model facets, while the hardware cost was only $1\%$ of that associated with LiDAR. Full article

Understanding the concept of dynamic aperture provides essential insights into nonlinear beam dynamics, beam losses, and the beam lifetime in circular particle accelerators. This comprehension is crucial for the functioning of modern hadron synchrotrons like the CERN Large Hadron Collider and the planning of future ones such as the Future Circular Collider. The dynamic aperture defines the extent of the region in phase space where the trajectories of charged particles are bounded over numerous revolutions, the actual number being defined by the physical application. Traditional methods for calculating the dynamic aperture depend on computationally demanding numerical simulations, which require tracking over multiple turns of numerous initial conditions appropriately distributed in phase space. Prior research has shown the efficiency of a multilayer perceptron network in forecasting the dynamic aperture of the CERN Large Hadron Collider ring, achieving a remarkable speed-up of up to 200-fold compared to standard numerical tracking tools. Building on recent advancements, we conducted a comparative study of various deep learning networks based on BERT, DenseNet, ResNet and VGG architectures. The results demonstrate substantial enhancements in the prediction of the dynamic aperture, marking a significant advancement in the development of more precise and efficient surrogate models of beam dynamics. Full article

The rapid evolution of deep learning has led to significant achievements in computer vision, primarily driven by complex convolutional neural networks (CNNs). However, the increasing depth and parameter count of these networks often result in overfitting and elevated computational demands. Knowledge distillation (KD) has emerged as a promising technique to address these issues by transferring knowledge from a large, well-trained teacher model to a more compact student model. This paper introduces a novel knowledge distillation method that simplifies the distillation process and narrows the performance gap between teacher and student models without relying on intricate knowledge representations. Our approach leverages a unique teacher network architecture designed to enhance the efficiency and effectiveness of knowledge transfer. Additionally, we introduce a streamlined teacher network architecture that transfers knowledge effectively through a simplified distillation process, enabling the student model to achieve high accuracy with reduced computational demands. Comprehensive experiments conducted on the CIFAR-10 dataset demonstrate that our proposed model achieves superior performance compared to traditional KD methods and established architectures such as ResNet and VGG networks. The proposed method not only maintains high accuracy but also significantly reduces training and validation losses. Key findings highlight the optimal hyperparameter settings (temperature T = 15.0 and smoothing factor α = 0.7), which yield the highest validation accuracy and lowest loss values. This research contributes to the theoretical and practical advancements in knowledge distillation, providing a robust framework for future applications and research in neural network compression and optimization. The simplicity and efficiency of our approach pave the way for more accessible and scalable solutions in deep learning model deployment. Full article

The purpose of this research is to contribute to the development of approaches for the classification and segmentation of various gastrointestinal (GI) cancer diseases, such as dyed lifted polyps, dyed resection margins, esophagitis, normal cecum, normal pylorus, normal Z line, polyps, and ulcerative colitis. This research is relevant and essential because of the current challenges related to the absence of efficient diagnostic tools for early diagnostics of GI cancers, which are fundamental for improving the diagnosis of these common diseases. To address the above challenges, we propose a new hybrid segmentation model, U-MaskNet, which is a combination of U-Net and Mask R-CNN models. Here, U-Net is utilized for pixel-wise classification and Mask R-CNN for instance segmentation, together forming a solution for classifying and segmenting GI cancer. The Kvasir dataset, which includes 8000 endoscopic images of various GI cancers, is utilized to validate the proposed methodology. The experimental results clearly demonstrated that the novel proposed model provided superior segmentation compared to other well-known models, such as DeepLabv3+, FCN, and DeepMask, as well as improved classification performance compared to state-of-the-art (SOTA) models, including LeNet-5, AlexNet, VGG-16, ResNet-50, and the Inception Network. The quantitative analysis revealed that our proposed model outperformed the other models, achieving a precision of 98.85%, recall of 98.49%, and F1 score of 98.68%. Additionally, the novel model achieved a Dice coefficient of 94.35% and IoU of 89.31%. Consequently, the developed model increased the accuracy and reliability in detecting and segmenting GI cancer, and it was proven that the proposed model can potentially be used for improving the diagnostic process and, consequently, patient care in the clinical environment. This work highlights the benefits of integrating the U-Net and Mask R-CNN models, opening the way for further research in medical image segmentation. Full article

This study proposes a deep learning-based method for monitoring the growth of Botrytis cinerea and evaluating the effectiveness of control measures. It aims to address the limitations of traditional statistical analysis methods in capturing non-linear relationships and multi-factor synergistic effects. The method integrates colony growth environment data and images as network inputs, achieving real-time prediction of colony area through an improved RepVGG network. The innovations include (1) combining channel attention mechanism, multi-head self-attention mechanism, and multi-scale feature extractor to improve prediction accuracy and (2) introducing the Shapley value algorithm to achieve a precise quantitative analysis of environmental variables’ contribution to colony growth. Experimental results show that the validation loss of this method reaches 0.007, with a mean absolute error of 0.0148, outperforming other comparative models. This study enriches the theory of gray mold control and provides information technology for optimizing and selecting its inhibitors. Full article

Skin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease is critical because it increases the effectiveness of treatment, which contributes to improved patient prognosis and reduced healthcare costs. Visual assessment and histopathological examination are the gold standards for diagnosing these types of lesions. Nevertheless, these processes are strongly dependent on dermatologists’ experience, with excision advised only when cancer is suspected by a physician. Multiple approaches have surfed over the last few years, particularly those based on deep learning (DL) strategies, with the goal of assisting medical professionals in the diagnosis process and ultimately diminishing diagnostic uncertainty. This systematic review focused on the analysis of relevant studies based on DL applications for skin cancer diagnosis. The qualitative assessment included 164 records relevant to the topic. The AlexNet, ResNet-50, VGG-16, and GoogLeNet architectures are considered the top choices for obtaining the best classification results, and multiclassification approaches are the current trend. Public databases are considered key elements in this area and should be maintained and improved to facilitate scientific research. Full article

In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted the task of collecting sound data in actual production environments, considering noise and complex surroundings. The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms’ deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). Results indicate that the fusion of cross-spectral features significantly improved classification performance, achieving a classification accuracy of 96.47%. These findings highlight the value of integrating acoustic features with spectrogram deep features for accurately recognizing sheep feeding behavior. Full article

One of the most common diseases afflicting humans is knee osteoarthritis (KOA). KOA occurs when the knee joint cartilage breaks down, and knee bones start rubbing together. The diagnosis of KOA is a lengthy process, and missed diagnosis can have serious consequences. Therefore, the diagnosis of KOA at an initial stage is crucial which prevents the patients from Severe complications. KOA identification using deep learning (DL) algorithms has gained popularity during the past few years. By applying knee X-ray images and the Kellgren–Lawrence (KL) grading system, the objective of this study was to develop a DL model for detecting KOA. This study proposes a novel model based on CNN called knee osteoarthritis classification network (KOC_Net). The KOC_Net model contains 05 convolutional blocks, and each convolutional block has three components such as Convlotuioanl2D, ReLU, and MaxPooling 2D. The KOC_Net model is evaluated on two publicly available benchmark datasets which consist of X-ray images of KOA based on the KL grading system. Additionally, we applied contrast-limited adaptive histogram equalization (CLAHE) methods to enhance the contrast of the images and utilized SMOTE Tomek to deal with the problem of minority classes. For the diagnosis of KOA, the classification performance of the proposed KOC_Net model is compared with baseline deep networks, namely Dense Net-169, Vgg-19, Xception, and Inception-V3. The proposed KOC_Net was able to classify KOA into 5 distinct groups (including Moderate, Minimal, Severe, Doubtful, and Healthy), with an AUC of 96.71%, accuracy of 96.51%, recall of 91.95%, precision of 90.25%, and F1-Score of 96.70%. Dense Net-169, Vgg-19, Xception, and Inception-V3 have relative accuracy rates of 84.97%, 81.08%, 87.06%, and 83.62%. As demonstrated by the results, the KOC_Net model provides great assistance to orthopedics in making diagnoses of KOA. Full article