VGG16 Network Research Articles

Deep Learning to Distinguish Edema Secondary to Retinal Vein Occlusion and Diabetic Macular Edema: A Multimodal Approach Using OCT and Infrared Imaging

Background: Retinal diseases are emerging as a significant health concern, making early detection and prompt treatment crucial to prevent visual impairment. Optical coherence tomography (OCT) is the preferred imaging modality for non-invasive diagnosis. Both diabetic macular edema (DME) and macular edema secondary to retinal vein occlusion (RVO) present an increase in retinal thickness, posing etiologic diagnostic challenges for non-specialists in retinal diseases. The lack of research on deep learning classification of macular edema secondary to RVO using OCT images motivated us to propose a convolutional neural network model for this task. Methods: The VGG-19 network was fine-tuned with a public dataset to classify OCT images. This network was then used to develop three models: unimodal—the input is only the OCT B-scan; multimodal—the inputs are the OCT B-scan and diabetes information, and multi-image—the inputs are the OCT B-scan, the infrared image, and the diabetes information. Seven hundred sixty-six patients from ULS São João were selected, comprising 208 healthy eyes, 207 with macular edema secondary to RVO, 218 with DME, and 200 with other pathologies. The performance metrics include accuracy, precision, recall, F0.5 score, and area under the receiver operating characteristic curve (AUROC). Results: The multi-image model achieved better results, with an accuracy of 95.20%, precision of 95.43%, recall of 95.20%, F0.5-score of 95.32%, F1-score of 95.21%, and AUROC of 99.59% on the classification task between four classes. Conclusions: This study presents a novel method to distinguish macular edema secondary to RVO and DME using diabetes diagnosis, OCT, and infrared images. This research aims to provide a reliable tool for ophthalmologists, improving the accuracy and speed of diagnoses.

An Infrared and Visible Image Fusion Network Based on Res2Net and Multiscale Transformer

The aim of infrared and visible image fusion is to produce a composite image that can highlight the infrared targets and maintain plentiful detailed textures simultaneously. Despite the promising fusion performance of current deep-learning-based algorithms, most fusion algorithms highly depend on convolution operations, which limits their capability to represent long-range contextual information. To overcome this challenge, we design a novel infrared and visible image fusion network based on Res2Net and multiscale Transformer, called RMTFuse. Specifically, we devise a local feature extraction module based on Res2Net (LFE-RN) in which dense connections are adopted to reuse the information that might be lost in convolution operation and a global feature extraction module based on multiscale Transformer (GFE-MT) which is composed of a Transformer module and a global feature integration module (GFIM). The Transformer module extracts the coarse-to-fine semantic features of the source images, while GFIM is used to further aggregate the hierarchical features to strengthen contextual feature representations. Furthermore, we employ the pre-trained VGG-16 network to compute the loss of features with different depths. Massive experiments on mainstream datasets indicate that RMTFuse is superior to the state-of-the-art methods in both subjective and objective assessments.

An Introspective Study on Attention-Based Transfer Learning in CNNs for Alzheimer's Disease Detection

Introduction: Deep learning (DL) algorithms have demonstrated remarkable advancements in the field of medical image analysis, particularly in the classification of Alzheimer’s Disease (AD). Despite these advancements, a significant challenge remains in acquiring the extensively annotated image datasets required to effectively train DL models. Attention mechanisms have emerged as powerful tools in DL, enabling models to prioritize critical regions within data and extract essential features. This focus not only enhances training efficiency but also improves overall classification performance. In this study, convolutional neural networks (CNNs) are utilized as the foundational architecture for AD classification tasks. To further enhance their performance, the Convolutional Block Attention Module (CBAM) is integrated into the CNN framework. CBAM is a lightweight and versatile attention mechanism that can be easily incorporated into any CNN architecture with minimal computational cost. By emphasizing important spatial and channel-wise features, CBAM significantly improves the feature extraction capability of CNNs. Building on this concept, an enhanced version of CBAM, referred to as enCBAM, is proposed. EnCBAM optimizes the generation of output feature maps, further improving the discriminative power of CNN architectures. In this work, the pre-trained VGG-16 network is employed as the base CNN model. When combined with enCBAM, the resulting architecture, referred to as EnCNN, achieves a substantial boost in classification performance. Specifically, EnCNN attains an impressive classification accuracy of 95.06%, outperforming its standalone counterpart and demonstrating the effectiveness of the enhanced attention mechanism.

Application of Convolutional Neural Networks for Watermelon Detection in UAV Aerial Images: A Case Study

Although human dependence on agriculture decreases with developing technology, it continues. As many resources are increasingly restricted due to various climatic reasons, the importance of studies in this field increases. Applications using deep learning models are frequently encountered in the agricultural field. In particular, there are applications where deep learning models are used as a tool for optimum planting, land use, yield improvement, production/disease/pest control, and other activities.In this study, watermelons in an aerial view of a watermelon field were detected by utilizing the Alexnet deep learning architecture. To obtain yield, watermelons in watermelon fields should be specified and then counted. Aerial images are used for this application. The field image was divided into 50% overlapping sub-images, and each was classified as watermelon, leaf, and soil. Consequently, watermelon regions on the field image were specified. After training the Alexnet and Vgg19 network structure with the dataset, watermelons were to be identified by segmenting the images. It was observed that the Vgg19 network achieved 97.78% accuracy. The results of the experimental applications show that the Vgg19 can be applied for watermelon fruit and yield detection applications.

Maximizing Data and Hardware Reuse for HLS with Early-Stage Symbolic Partitioning

While traditional HLS (High-Level Synthesis) converts “high-level” C-like programs into hardware automatically, producing high-performance designs still requires hardware expertise. Optimizations such as data partitioning can have a large impact on performance since they directly affect data reuse patterns and the ability to reuse hardware. However, optimizing partitioning is a difficult process since minor changes in the parameter choices can lead to totally unpredictable performance. Functional array-based languages have been proposed instead of C-based approaches, as they offer stronger performance guarantees. This paper proposes to follow a similar approach and exposes a divide-and-conquer primitive at the algorithmic level to let users partition any arbitrary computation. The compiler is then free to explore different partition shapes to maximize both data and hardware reuse automatically. The main challenge remains that the impact of partitioning is only known much later in the compilation flow. This is due to the hard-to-predict effects of the many optimizations applied during compilation. To solve this problem, the partitioning is expressed using a set of symbolic tunable parameters, introduced early in the compilation pipeline. A symbolic performance model is then used in the last compilation stage to predict performance based on the possible values of the tunable parameters. Using this approach, a design space exploration is conducted on an Intel Arria 10 FPGAs (Field Programmable Gate Arrays), and competitive performance is achieved on the classical VGG and TinyYolo neural networks.

Physics-informed hybrid models for enhanced precision in breast cancer classification

Breast cancer poses a significant health and safety risk for women globally, making early detection vital for effective treatment. Artificial intelligence (AI) can enhance early detection by differentiating between cancerous and non-cancerous breast tissues. Existing AI approaches for breast cancer detection face limitations, including overfitting, the need for fine-tuning, and loss of fine details. This research introduces an adaptive hybrid model infused with physics insights framework to address these challenges. The transformed dynamic and adaptive filter is proposed for data preprocessing and achieving a balance between noise reduction and edge preservation, thereby retaining critical image structures. Then, robotic physics informed model is proposed which contains diffusion convolution, batch normalization layers, activation layers, pooling layers, optimization layer and ends with a classification layer. The proposed approach compared with the baseline AOADL-HBCC, DTLRO-HCBC, Inception v3, Inception v3 Long Short Term Memory, Inception v3 Bi-directional Long Short Term Memory, VGG-16, and Residual Network such as 96.77%, 93.52%, 81.67%, 91.46%, 92.05%, 80.15%, and 82.18%, respectively. The accuracy of the proposed approach is 99.56%. This demonstrates our model’s superior performance and effectiveness in breast cancer detection.

Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays

Convolutional neural networks (CNNs) are increasingly recognized as an important and potent artificial intelligence approach, widely employed in many computer vision applications, such as facial recognition. Their importance resides in their capacity to acquire hierarchical features, which is essential for recognizing complex patterns. Nevertheless, the intricate architectural design of CNNs leads to significant computing requirements. To tackle these issues, it is essential to construct a system based on field-programmable gate arrays (FPGAs) to speed up CNNs. FPGAs provide fast development capabilities, energy efficiency, decreased latency, and advanced reconfigurability. A facial recognition solution by leveraging deep learning and subsequently deploying it on an FPGA platform is suggested. The system detects whether a person has the necessary authorization to enter/access a place. The FPGA is responsible for processing this system with utmost security and without any internet connectivity. Various facial recognition networks are accomplished, including AlexNet, ResNet, and VGG-16 networks. The findings of the proposed method prove that the GoogLeNet network is the best fit due to its lower computational resource requirements, speed, and accuracy. The system was deployed on three hardware kits to appraise the performance of different programming approaches in terms of accuracy, latency, cost, and power consumption. The software programming on the Raspberry Pi-3B kit had a recognition accuracy of around 70–75% and relied on a stable internet connection for processing. This dependency on internet connectivity increases bandwidth consumption and fails to meet the required security criteria, contrary to ZYBO-Z7 board hardware programming. Nevertheless, the hardware/software co-design on the PYNQ-Z2 board achieved an accuracy rate of 85% to 87%. It operates independently of an internet connection, making it a standalone system and saving costs.

Advanced analytical methods for multi-spectral transmission imaging optimization: enhancing breast tissue heterogeneity detection and tumor screening with hybrid image processing and deep learning.

Light sources exhibit significant absorption and scattering effects during the transmission through biological tissues, posing challenges in identifying heterogeneities in multi-spectral images. This paper introduces a fusion of techniques encompassing the spatial pyramid matching model (SPM), modulation and demodulation (M_D), and frame accumulation (FA). These techniques not only elevate image quality but also augment the precision of heterogeneous classification in multi-spectral transmission images (MTI) within deep learning network models (DLNM). Initially, experiments are designed to capture MTI of phantoms. Subsequently, the images are preprocessed separately through a combination of different techniques such as SPM, M_D and FA. Ultimately, multi-spectral fusion pseudo-color images derived from U-Net semantic segmentation are fed into VGG16/19 and ResNet50/101 networks for heterogeneous classification. Among them, different combinations of SPM, M_D and FA significantly enhance the quality of images, facilitating the extraction of heterogeneous feature information from multi-spectral images. In comparison to the classification accuracy achieved in the original image VGG and ResNet network models, all images after preprocessing effectively improved the classification accuracy of heterogeneities. Following scatter correction, images processed with 3.5 Hz modulation-demodulation combined with frame accumulation (M_D-FA) attain the highest classification accuracy for heterogeneities in the VGG19 and ResNet101 models, achieving accuracies of 95.47% and 98.47%, respectively. In conclusion, this paper utilizes different combinations of SPM, M_D and FA techniques to not only enhance the quality of images but also further improve the accuracy of DLNM in heterogeneous classification, which will promote the clinical application of MTI technique in breast tumor screening.

Research on automatic modulation recognition of shortwave signals in few‐shot scenarios based on knowledge distillation

AbstractAutomatic modulation recognition plays an important role in wireless communication and radio regulation. Existing deep learning‐based automatic modulation recognition techniques perform well with large datasets and high computational power but require significant resources for labelled data and complex pre‐processing. This paper proposes a multi‐information fusion method for few‐shot modulation recognition, which involves converting I/Q signals into A/P signals and training with a combination of VGG and LSTM network models. An ensemble knowledge distillation (EKD) approach is employed to streamline the network model, meeting the demands for deploying neural network models on compact devices. Experimental results demonstrate that using only 1% of the shortwave modulation signal dataset as the training set, the proposed model achieves an average classification accuracy of 71.08% under all signal‐to‐noise ratios, surpassing the currently popular deep learning models. Moreover, two small‐scale networks, MobileNetV3 and convolutional neural network are trained, through EKD. Compared to the teacher network, the floating‐point operations of the distilled models are reduced by 99.8% and 99.7%, respectively, and the average prediction accuracy only decreases by 16.05% and 8.09%. The lightweight, few‐shot networks designed in this study for shortwave modulation signals aim to achieve fast and accurate modulation recognition on compact devices.

OsteoNet - A Framework for identifying Osteoporosis in Bone Radiograph Images Using Attention Based VGG Network

OsteoNet - A Framework for identifying Osteoporosis in Bone Radiograph Images Using Attention Based VGG Network

An LSTM and Conventional, Global, and Object-Based Semantic Feature Fusion Framework for Indoor-Outdoor Scene Classification

This article proposes a novel approach that uses diverse features from a scene image using a variety of local, global, and object-based descriptors. The regional and image-based features are obtained using various conventional statistical descriptors, whereas a deep network VGG19 is used to extract the scene's global features. Scene objects based on specific features are concatenated to regional and global features after they are segmented using another deep network, YOLOV5m. While conventional features represent intensity-based characteristics, global and object features carry color depth details of the scene image. A long-term short memory (LSTM) network with a fully connected (FC) dense layer network is trained over images from four benchmark cross-datasets. Experimental evaluation over 5081 indoor-outdoor scene images showed that the proposed scene classification approach obtained 96% accuracy in identifying both categories on 15% test sample images.

Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review

Crop diseases pose a significant threat to global food security, with both economic and environmental consequences. Early and accurate detection is essential for timely intervention and sustainable farming. This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. The review method includes an in-depth analysis of algorithm performance using key metrics such as accuracy, precision, recall, and F1 score across various datasets. We also highlight the data imbalances in commonly used datasets, particularly PlantVillage, and discuss the challenges posed by these imbalances. The research highlights critical insights regarding ML and DL models in crop disease detection. A primary challenge identified is the imbalance in the PlantVillage dataset, with a high number of healthy images and a strong bias toward certain disease categories like fungi, leaving other categories like mites and molds underrepresented. This imbalance complicates model generalization, indicating a need for preprocessing steps to enhance performance. This study also shows that combining Vision Transformers (ViTs) with Green Chromatic Coordinates and hybridizing these with SVM achieves high classification accuracy, emphasizing the value of advanced feature extraction techniques in improving model efficacy. In terms of comparative performance, DL architectures like ResNet50, VGG16, and convolutional neural network demonstrated robust accuracy (95–99%) across diverse datasets, underscoring their effectiveness in managing complex image data. Additionally, traditional ML models exhibited varied strengths; for instance, SVM performed better on balanced datasets, while RF excelled with imbalanced data. Preprocessing methods like K-means clustering, Fuzzy C-Means, and PCA, along with ensemble approaches, further improved model accuracy. Lastly, the study underscores that high-quality, well-labeled datasets, stakeholder involvement, and comprehensive evaluation metrics such as F1 score and precision are crucial for optimizing ML and DL models, making them more effective for real-world applications in sustainable agriculture.

Detecting Wheat Leaf Diseases: A Deep Feature-Based Approach with Machine Learning Classification

Wheat is a rich storehouse of nutrients with many different vitamins and minerals. Wheat is one of the main cereals that meet the nutritional needs of humans and other living things and is used in the production of other foods. It can be grown in almost all regions of the world. It requires less irrigation compared to other plants. One of the most important problems in wheat cultivation is the fight against diseases. Wheat diseases cause yield losses and quality decreases as in other agricultural products. Timely and accurate diagnosis of these diseases; It is clear that it will lead to an increase in yield and quality. Detection of these diseases with the naked eye can be difficult and laborious. In this study, diseases on wheat leaves were detected using image processing techniques. The features of septoria and stripe rust diseases on wheat leaves were extracted using pre-trained networks VGG-16, VGG-19 and then classified with machine learning algorithms support vector machines (SVM), multi-layer perceptron (MLP), k-nearest neighbor (KNN). The results obtained were evaluated with performance criteria such as accuracy, sensitivity, specificity, precision and F1-Score. In the analysis, the features extracted with VGG-19 were classified with SVM method and the highest classification accuracy of 98.63% was achieved.

Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network.

Although approaches for the online surface detection of automotive pipelines exist, low defect area rates, small-sample and long-tailed data, and the difficulty of detection due to the variable morphology of defects are three major problems faced when using such methods. In order to solve these problems, this study combines traditional visual detection methods and deep neural network technology to propose a transfer learning multi-channel fusion decision network without significantly increasing the number of network layers or the structural complexity. Each channel of the network is designed according to the characteristics of different types of defects. Dynamic weights are assigned to achieve decision-level fusion through the use of a matrix of indicators to evaluate the performance of each channel's recognition ability. In order to improve the detection efficiency and reduce the amount of data transmission and processing, an improved ROI detection algorithm for surface defects is proposed. It can enable the rapid screening of target surfaces for the high-quality and rapid acquisition of surface defect images. On an automotive pipeline surface defect dataset, the detection accuracy of the multi-channel fusion decision network with transfer learning was 97.78% and its detection speed was 153.8 FPS. The experimental results indicate that the multi-channel fusion decision network could simultaneously take into account the needs for real-time detection and accuracy, synthesize the advantages of different network structures, and avoid the limitations of single-channel networks.

Lung CT Image Segmentation Using VGG-16 Network with Image Enhancement Based on Bounded Turning Mittag-Leffler Function

يعتبر الاستقطاع التلقائي للأمراض خطوة أولية ضرورية في التشخيص الروتيني. تعتبر أمراض الرئة التي تؤثر على الرئتين، مثل الالتهاب الرئوي أو التليف الرئوي والذي يؤدي الى كثافة في أنسجة الرئة والتي تجعل عملية استقطاع هذه المناطق عملية صعبه جدا بسبب الخصائص المتشابه للانسجة المجاورة. لذا تقترح هذه الدراسة نموذج جديد لاستقطاع الصور المقطعية للرئة والتي تتضمن خطوتين: (1) استخدام نموذج bounded turning Mittag-Leffler لتحسين الصور المقطعية للحصول على نتائج استقطاع أفضل. (2) استخدام شبكة VGG-16 المعدلة لاستقطاع المناطق المصابة بالعدوى في الصور المقطعية للرئة. حيث تم اضافة مجموعة من الطبقات التلافيفية المتوسعة إلى شبكة VGG-16 الأصلية لإنشاء طريقة جديدة لتجزئة صورة الرئة المقطعية. وقد حققت الطريقة المقترحة في هذه الدراسة دقه عالية في عملية الاستقطاع حيث كان معدل الدقه وDice Coefficient وJaccard Index هو 96.3%، 91.2%، و82.3% على التوالي. وقد تم مقارنة النتائج المنجزة مع الدراسات السابقة وقد أظهر هذا مدى جودة أداء هذه الدراسة مقارنة بالعديد من الدراسات السابقة على الرغم من ان الحصول على مستوى مناسب من الدقة أمرًا صعبًا للغاية.

Evaluation of myocardial perfusion imaging techniques and artificial intelligence (AI) tools in coronary artery disease (CAD) diagnosis through multi-criteria decision-making method.

Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years. This study aims to employ a novel approach to examine how parameters/criteria and performance metrics affect the prioritization of selected MPI techniques and AI tools in CAD diagnosis. Identifying the most effective method in these two interconnected areas will increase the CAD diagnosis rate. The study includes an in-depth investigation of popular convolutional neural network (CNN) models, including InceptionV3, VGG16, ResNet50, and DenseNet121, in addition to widely used machine learning (ML) models, including random forests (RF), K-nearest neighbor (KNN), support vector machine (SVM), and Naïve Bayes (NB). In addition, it includes the evaluation of nuclear MPI techniques, including positron emission tomography (PET) and single photon emission computed tomography (SPECT), with the non-nuclear MPI technique of cardiovascular magnetic resonance imaging (CMR). Various performance metrics were used to evaluate AI tools. They are F1-score, recall, specificity, precision, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). For MPI techniques, the evaluation criteria include specificity, sensitivity, radiation dose, cost of scan, and study duration. The analysis was evaluated and compared using the fuzzy-based preference ranking organization method for enrichment evaluation (PROMETHEE), the multi-criteria decision-making method (MDCM). According to the study's findings, considering selected performance metrics or criteria, RF is the most efficient AI tool for SPECT MPI in the diagnosis of CAD with a net flow (Φnet ) of 0.3778, and it's revealed that CMR is the most efficient MPI technique for CAD diagnosis with a net flow of 0.3666. By expanding this study, more comprehensive evaluations can be made in the diagnosis of CAD. It was concluded that CMR outperformed the nuclear MPI techniques. SPECT, as the least advantageous technique, remained below average on other criteria except for the cost of the scan. Integrating the RF algorithm, which stands out as the most effective AI tool in diagnosing CAD, with SPECT MPI may contribute to SPECT becoming a superior alternative.

Advancements in Arabic Sign Language Recognition: A Method based on Deep Learning to Improve Communication Access

This research anticipates the issue of real-time sign language recognition as a means of facilitating the daily communication of the hearing-impaired persons. Improvement in the rate of real time sign language recognition will help in boosting interaction in the society and access to services for the hearing-impaired persons. This paper outlines a step-by-step guide on fine tuning an object detection model for the AASL using the SSD architecture. The AASL dataset which includes total of 7,857 samples of more than 200 patients are first preprocessed where the images are scaled, normalized, and augmented. In SSD model, the base of the network is the VGG16 network and few extra layers for feature extraction and for auxiliary and prediction of objects are added. Particularly in object detection, Intersection over Union (IoU), and mean Average Precision (mAP) are used in addition to the confusion matrix metrics comprising accuracy, precision, recall, and F1-score. The proposed model provides good recognition accuracy of 98 percent. This feature checks its capability of real-time identification of sign languages and shows 25% efficiency. The efficiency of the proposed method is explained when comparing the results of the modified VGG16-based SSD with other methodologies. As for the improvement of communication for the hearing-impaired individuals, the present work demonstrates that the deep learning methods can be significantly effective; however, suggesting that more research efforts should be directed to real-time solutions and that the datasets should be expanded.

Efficient Layer Optimizations for Deep Neural Networks

Deep neural networks (DNNs) have technical issues such as long training time as the network size increases. Parameters require significant memory, which may cause migration issues for embedded devices. DNNs applied various pruning techniques to reduce the network size in deep neural networks, but many problems still exist when applying the pruning techniques. Among neural networks, several applications applied autoencoders for reconstruction and dimension reduction. However, network size is a disadvantage of autoencoders since the architecture of the autoencoders has a double workload due to the encoding and decoding processes. In this research, we chose autoencoders and two deep neural networks AlexNet and VGG16 to apply out of order layer pruning. We perform the sensitivity analysis to explore the performance variations for the network architecture and network complexity through an out of order layer pruning mechanism. As a result of applying the proposed layer pruning scheme to the autoencoder, we developed the accordion autoencoder (A2E) and applied credit card fraud detection and MNIST classification. Our results show 4.9 Percent and 13.6 Percent performance drops, respectively, but we observe a significant reduction in network complexity, 85.1 Percent and 94.5 Percent for each application. We extend the out of order layer pruning to deeper learning networks. In our approach, we propose a simple yet efficient scheme, accuracy aware structured filter pruning based on the characterization of each convolutional layer combined with the quantization of fully connected layers. We investigate the accuracy and compression rate of each layer using a fixed pruning ratio, and then the pruning priority is rearranged depending on the accuracy of each layer. Our analysis of layer characterization shows that the pruning order of the layers does affect the final accuracy of the deep neural network. Based on our experiments using the proposed pruning scheme, the parameter size in the AlexNet can be up to 47.28x smaller than the original model. We also obtained comparable results for VGG16, achieving a maximum compression rate of 35.21x.

Sleep Apnea Detection

Sleep apnea is one of the most common sleep disorders, posing a significant health risk. The proposed paper provides a prospective technique by applying machine learning to enable the early prediction of sleep apnea. Two methodologies were used for this study: Firstly, the hybrid model was used in analyzing the electrocardiogram records by combining VGG16 and Long Short-Term Memory (LSTM) networks for the extraction of meaningful features from the ECG signals for modeling the process of aiding detection. Second, lifestyle patterns were assessed in their relationship with the disorder. Many lifestyle factors were analyzed in search of critical indicators that may, at an early age, indicate the onset of the case of sleep apnea. The fusion of deep learning methods and analysis of lifestyle patterns offers a comprehensive framework for the solution to the complexity of sleep apnea detection. More exactly, this kind of interdisciplinary approach should permit improvement in effectiveness and accuracy for early diagnosis, therefore enabling timely intervention and treatment of that effect. Proposed here is an innovative methodology that could lead to better management and recovery of the affected.

UATST: Towards unpaired arbitrary text-guided style transfer with cross-space modulation

Existing style transfer methods usually utilize style images to represent the target style. Since style images need to be prepared in advance and are confined to existing artworks, these methods are limited in flexibility and creativity. Compared with images, language is a more natural, common, and flexible way for humans to transmit information. Therefore, a better choice is to utilize text descriptions instead of style images to represent the target style. To this end, we propose a novel Unpaired Arbitrary Text-guided Style Transfer (UATST) framework, which can render arbitrary photographs in the style of arbitrary text descriptions with one single model. To the best of our knowledge, this is the first model that achieves Arbitrary-Text-Per-Model with unpaired training data. In detail, we first use a pre-trained VGG network to map the content image into the VGG feature space, and use a pre-trained CLIP text encoder to map the text description into the CLIP feature space. Then we introduce a cross-space modulation module to bridge these two feature spaces, so that the content and style information in two different spaces can be seamlessly and adaptively combined for stylization. In addition, to learn better style representations, we introduce a new CLIP-based style contrastive loss to our model. Extensive qualitative and quantitative experiments verify the effectiveness and superiority of our method.