Multiple Convolutional Neural Network Models Research Articles

Earthquake damage detection and level classification method for wooden houses based on convolutional neural networks and onsite photos

AbstractThe results of earthquake damage certification (EDC) surveys are the basis of support measures for improving the lives of disaster victims. To address issues such as a limited workforce to perform EDC surveys and difficulties in judging the level of damage, a damage detection and level classification method for wooden houses using multiple convolutional neural network models is proposed. The proposed method, including detection, filtering, and classification models, was trained and validated based on photographs collected from EDC surveys in Uki City, Kumamoto Prefecture. Then, a software system, which deployed these models, was developed for the onsite EDC surveyors to detect damages shown in the photographs of the surveyed house and classify damage levels. The test results based on 32 target buildings indicate that the detection model achieved high recall in detecting damage. Moreover, the redundant detected regions can be precisely filtered by the filtering model. Finally, the classification model achieved relatively high overall accuracy in classifying the damage level.

Improving Speech Recognition with Convolutional Neural Networks

This project explores advanced techniques in speech recognition, focusing on emotion identification using Convolutional Neural Networks for improved accuracy and real-time processing efficiency. Emotion recognition from speech signals plays a crucial role in various applications, including human-computer interaction, customer service, mental health monitoring, and entertainment. This project proposes an innovative approach to emotion recognition using Convolutional Neural Networks (CNNs) applied to speech data. By leveraging advanced deep learning techniques, the proposed system aims to accurately identify and classify emotions conveyed through vocal expressions. The project begins with a comprehensive review of existing literature on emotion recognition and speech processing, identifying key challenges and opportunities in the field. Building upon prior research, the project introduces a novel CNN architecture optimized for emotion recognition tasks. This architecture is designed to extract relevant features from speech signals and capture subtle nuances indicative of different emotional states. One of the distinguishing features of the proposed approach is its multi-modal integration, which combines information from both audio and visual modalities to enhance emotion recognition accuracy. In addition to analysing speech signals, the system incorporates visual cues such as facial expressions and gestures, providing a more comprehensive understanding of the speaker's emotional state. Real-time processing efficiency is prioritized in the design of the system, ensuring prompt and responsive emotion recognition in interactive applications. Optimization techniques such as model quantization and lightweight architecture design are employed to minimize computational overhead while maintaining high accuracy. To address the variability and subjectivity of emotional expression, the system incorporates user-specific adaptation mechanisms. Through continuous learning and feedback integration, the system dynamically adapts to individual speakers' speech patterns and emotional characteristics, enhancing its ability to accurately recognize emotions in diverse contexts. The project also explores ensemble learning strategies to improve robustness and generalization performance. By combining predictions from multiple CNN models trained on diverse datasets, the system achieves greater resilience to variations in emotional expression and environmental factors. Ethical considerations, including privacy protection and responsible data handling, are integral aspects of the project's design and implementation. Measures are implemented to ensure the ethical collection, storage, and usage of speech data, safeguarding user privacy and maintaining trust in the system. Overall, the proposed system represents a significant advancement in emotion recognition technology, offering a sophisticated and versatile solution for accurately identifying emotions from speech signals. By leveraging deep learning techniques, multi-modal integration, real-time processing optimization, user-specific adaptation, and ensemble learning, the system demonstrates promising potential for various practical applications requiring robust and context-aware emotion recognition capabilities. Keywords: Speech Recognition, Emotion Identification, Convolutional Neural Networks (CNNs), Real-time Processing, Multi-modal Integration, User-specific Adaptation, Ensemble Learning, Deep Learning, Emotional Expression, Ethical Data Handling

Semantic-aware automatic extraction method for bottom sediment annotations in raster nautical charts

ABSTRACT The automatic extraction of bottom sediment annotations in large-scale raster nautical charts has limitations, including an imprecise semantic information description and low efficiency. To overcome them, we propose a convolutional neural network (CNN)-based method for the automatic extraction of bottom sediment annotations in raster nautical charts, using image processing techniques to improve it. First, an adaptive chart partitioning model that considers element completeness is constructed. Second, a principle for the unique identification of elements based on spatial conflicts is designed. Finally, a model for accurately extracting semantic information for bottom sediment annotations is established. To evaluate the effectiveness of the proposed method, we implemented a model based on the PyTorch framework and used the PIL library to analyze the results. We also conducted comparative experiments on multiple CNN models to recommend the selection of such models in the proposed method by comparing their classification and recognition performance. The experimental results indicate that (1) the proposed model can achieve high-precision extraction of bottom sediment annotations in raster nautical charts. (2) Furthermore, the proposed model generally has high recognition accuracy and semantic completeness, with better recognition precision than traditional pattern recognition methods.

P461 Deep learning model for endoscopic differential diagnosis between intestinal lymphoma and Crohn's disease: a multicenter study

Abstract Background Primary and secondary intestinal lymphoma (IL) are a relatively rare cancer, while easily misdiagnosed due to the unclear aetiology and unspecific clinical symptoms. Moreover, the endoscopic features of IL vary depending on pathological types, making it difficult to distinguish from Crohn's disease (CD). This study aims to develop deep learning models based on endoscopic images, facilitating the differential diagnosis of IL and CD. Methods We retrospectively recruited 271 patients with IL and 232 patients with CD from 6 tertiary centres in China. With out-of-focus or poor-bowel-prepared images excluded, we used 1739 IL and 1649 CD images for internal and external validation to build deep learning models. For internal validation, we used five-fold cross-validation with 1517 IL images and 1428 CD images from 5 centres randomly assigned without any patient overlap. 222 IL and 221 CD images from the 6th centre were used for independent external validation to measure the generalization ability of models. Further, we compared the performance of models with that of clinical physicians consisting of 2 junior physicians and 2 endoscopy specialists with more than ten years of clinical experience. We evaluated models using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) and used the kappa statistic to measure the inter-rater reliability between models and clinical physicians. Results We developed multiple convolutional neural network models, including ResNet18, ResNet34, ResNet50, VGG16, VGG19, DenseNet121, Inception v3, and Xception. The top 3 models in the internal validation were Inception v3, Xception, and DenseNet121. Inception v3 showed the best performance (accuracy: 83.1%; AUC: 0.910), while Xception performed better on average (average accuracy: 76.8%; average AUC: 0.834). In the external validation, the top 3 models were Inception v3, DenseNet121, and VGG19. Inception v3 achieved the highest accuracy of 81.9%, while DenseNet121 had the best AUC of 0.903. Compared with clinical physicians, Inception v3 performed better than junior physicians (average accuracy: 67.2%) and showed good agreement with the best endoscopy specialist (kappa: 0.604, p<0.05). Conclusion Convolutional neural network models using endoscopic images were able to differentiate PIL from CD, with better-diagnosing performance than junior physicians, good agreement with specialists, and good generation ability, which were expected to assist clinical physicians lacking experience to differentiate PIL and CD more precisely under endoscope.

Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery

The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies.

Concatenation-based pre-trained convolutional neural networks using attention mechanism for environmental sound classification

Environmental sound classification (ESC) is gaining popularity in the field of information processing due to its significance in non-speech audio categorization. ESC faces challenges in categorizing ambient sounds, which lack a clear structure, unlike speech or music sounds. Deep learning (DL) techniques are widely used in ESC to extract relevant information from ambient sounds, in which feature extraction is very crucial and directly impacts the classification performance. However, feature extraction can be computationally expensive, especially when employing complex non-linear techniques, due to the significant amount of computing resources required during the training process of DL models. Moreover, environmental sounds can exhibit significant variability in terms of their temporal and spectral characteristics, which can pose challenges to training DL models effectively. In order to overcome these limitations, this research proposes an efficient method for extracting meaningful features from audio files and improving DL techniques' performance using spectrogram images generated from sound environmental datasets. The proposed approach uses convolutional neural networks (CNNs) with attention mechanisms and appropriate data augmentation methods. Unlike pre-trained models that use a single vector for feature extraction, the proposed approach uses a new concatenation-based CNN model with attention mechanisms, which can more effectively capture intricate relationships between input data features. This approach allows for the extraction of features from different sectors of the feature space, resulting in a more precise classification of complex and diverse datasets. Additionally, the proposed approach leverages the parallel extraction feature technique to extract features from multiple CNN models, which improves classification performance. Furthermore, attention modules are used to focus on the most relevant features of the input data. Comparative experiments are also conducted for the proposed approach and some existing state-of-the-art methods, and the results show that the former has better classification performance.

Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias

BackgroundSkeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias.ObjectiveWe present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias.Materials and methodsWe trained multiple convolutional neural network models under various conditions and selected three to build a precise model ensemble. We utilized the public BA dataset from the Radiological Society of North America (RSNA) consisting of training, validation, and test subsets containing 12,611, 1,425, and 200 hand and wrist radiographs, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 radiographs from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including achondroplasia and hypochondroplasia. A subset of the dysplastic cohort (149 images) was used to estimate the test–retest precision of our model ensemble on longitudinal data.ResultsThe mean absolute difference of Deeplasia for the RSNA test set (based on the average of six different reference ratings) and dysplastic set (based on the average of two different reference ratings) were 3.87 and 5.84 months, respectively. The test–retest precision of Deeplasia on longitudinal data (2.74 months) is estimated to be similar to a human expert.ConclusionWe demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.Graphical

Investigation of multiple convolutional neural network models on emotion detection

The accurate detection of emotions holds significant importance in the field of psychology, necessitating the careful selection of an appropriate model for facial expression classification. In this study, emotion detection is the classification task to compare the performance of MobileNet, ResNet, and DenseNet. For the detailed model, MobileNet, ResNet50, and DenseNet169 are selected for comparative analysis. The dataset FER-2013 is from Kaggle, which contains a training set and test set consisting of 29, 709 samples and 3589 samples, respectively, with seven facial expression categories. In terms of preprocessing, normalization, and data augmentation are considered. The whole dataset is normalized by dividing 255 and augmented with a Keras image generator. In the model-building step, the structure of the test models is controlled in the same structure. The pre-trained model from the Keras application connects with one global average pooling layer and adds one dense layer at the last as the output layer with the SoftMax activation function. Moreover, this study kept all hyper all parameters the same during the training period. After the model training, the confusion matrix is used to show the class relativity and the loss and accuracy of each model are plotted for analysis. Experimental results demonstrated that the MobileNet achieves 56.08% accuracy on test set which is more competitive than the DenseNet169 and ResNet50 and provides a relatively stable loss.

Feature fusion-based ensemble CNN learning optimization for automated detection of pediatric pneumonia

Detecting pediatric pneumonia accurately and rapidly is crucial for timely treatment, especially considering its association with seasonal changes and potentially fatal outcomes. However, medical image analysis using convolutional neural network (CNN) models faces challenges such as limited labeled data, image noise, class imbalance, and overfitting. Regularization techniques are often insufficient, necessitating advanced approaches for successful pneumonia detection. Our study aims to accurately detect pneumonia by proposing an ensemble CNN framework that incorporates optimal feature fusion. A novel image preprocessing algorithm has been developed that applies hierarchical template-matching to reduce image noise and improves the learning of relevant features. Instead of relying solely on a few pre-defined CNN models combined through majority voting, multiple CNN models with different architectures are trained on the pneumonia dataset using fine-tuning and transfer learning techniques. To obtain an optimal feature set, Chi-Square and mRMR methods iteratively eliminate irrelevant features from the fully connected layer of each CNN model. These optimal feature sets are then concatenated to enhance feature vector diversity for classification. The study’s results illustrate that when compared to state-of-the-art approaches, this framework achieves exceptional accuracy (98.94%) and F1 score (99.12%) on a public test dataset. These findings strongly support the conclusion that the proposed feature fusion-based model outperforms individual models and the majority voting ensemble method in terms of performance. The significance of this successful approach to pneumonia detection lies in its potential to provide clinicians and healthcare professionals with an effective solution for accurate and rapid diagnosis, particularly in pediatric cases.

Fusing Local Shallow Features and Global Deep Features to Identify Beaks.

Cephalopods are an essential component of marine ecosystems, which are of great significance for the development of marine resources, ecological balance, and human food supply. At the same time, the preservation of cephalopod resources and the promotion of sustainable utilization also require attention. Many studies on the classification of cephalopods focus on the analysis of their beaks. In this study, we propose a feature fusion-based method for the identification of beaks, which uses the convolutional neural network (CNN) model as its basic architecture and a multi-class support vector machine (SVM) for classification. First, two local shallow features are extracted, namely the histogram of the orientation gradient (HOG) and the local binary pattern (LBP), and classified using SVM. Second, multiple CNN models were used for end-to-end learning to identify the beaks, and model performance was compared. Finally, the global deep features of beaks were extracted from the Resnet50 model, fused with the two local shallow features, and classified using SVM. The experimental results demonstrate that the feature fusion model can effectively fuse multiple features to recognize beaks and improve classification accuracy. Among them, the HOG+Resnet50 method has the highest accuracy in recognizing the upper and lower beaks, with 91.88% and 93.63%, respectively. Therefore, this new approach facilitated identification studies of cephalopod beaks.

Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface

BackgroundMotor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition.MethodsThis paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label.ResultsExperiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods.ConclusionThe proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

New weighted BERT features and multi-CNN models to enhance the performance of MOOC posts classification

Learning is an essential requirement for humans, and its means have evolved. Ten years ago, Massive Open Online Courses (MOOCs) were introduced, attracting many interests and learners. MOOCs provide forums for learners to interact with instructors and to express any problems they encounter in the educational process. However, MOOCs have a high dropout rate due to the difficulties of following up on learners' posts and identifying the urgent ones to react quickly. This research aims to assist instructors in automatically identifying urgent posts, making it easier to respond to such posts rapidly, increasing learner engagement, and improving course completion rate. In this paper, we propose a novel classification model for identifying urgent posts. The proposed model consists of four stages. In the first stage, the post-text is code-encoded and vectorized using a pre-trained BERT model. In the second stage, a novel feature aggregation model is proposed to reveal data-based relationships between token features and their representation in a higher-level feature. In the third stage, a novel model based on convolutional neural networks (CNNs) is proposed to reveal the meaning of a text context more accurately. In the last stage, the extracted composite features are used to classify the text of the post. Several experimental studies were conducted to get the best performance of the proposed stages of the system. The experimental results demonstrated the architectural efficiency of the proposed feature aggregation and multiple CNN models, as well as the accuracy of the proposed system compared to the current research.

Identification of Inability States of Rotating Machinery Subsystems Using Industrial IoT and Convolutional Neural Network – Initial Research

Rotating parts can be found in almost all operational equipment in the industry and are of great importance for proper operation. However, reliability theory explains that every industrial system can change its state when failure happens. Predictive maintenance as one of the latest maintenance strategy emerged from the Maintenance 4.0 concept. Nowadays, this concept can include Industrial Internet of Things (IIoT) devices to connect industrial assets thus enable data collection and analysis that can help make better decisions about maintenance activity. Robust data acquisition system is a prerequisite for any modern predictive maintenance task as it provides necessary data for further analysis and health assessment of the industry asset. Fault diagnosis is an important task in the maintenance of industrial rotating subsystems, considering that early state change diagnosis and fault identification can prevent system failure. Vibration analysis in theory and practice is considered a correct technique for early detection of state changes and failure diagnostics of rotating subsystems. The identified technical state should be considered in a context of the ability and different inability states. Therefore, early different inability states identification is the next step in the rotary machinery diagnostics procedure. Most of the existing techniques for fault diagnosis of rotating subsystems that use vibrations involve the step of extracting features from the raw signal. Considering that the features that describe the behavior of the rotary subsystem can differ significantly depending on the type of equipment, such an approach usually requires an expert in the field of signal processing and rotary subsystems who can define the necessary features. Recently, the emergence of machine deep learning and its application in maintenance promises to provide highly efficient fault diagnostics while simultaneously reducing the need for expert knowledge and human labour. This paper presents authors aim to use self-developed IIoT system built as an IIoT accelerometer as the edge device, web API and database with convolutional neural network as deep learning-based data-driven fault diagnosis to detect and identify different inability states of rotating subsystems. Large dataset for two different rotational speed is collected using IIOT system and multiple convolutional neural network models are trained and tested to examine possibility of using IIOT for inability state prediction.

Combination of Convolutional Neural Network and AdaBoost for Breast Cancer Diagnosis

Breast cancer is a cancer that develops from breast tissue. Early symptoms of breast cancer include the existence of a lump in the breast, a change in breast shape, or dimpling on the skin. This research explores the potential of ensemble learning, with a focus on the AdaBoost algorithm, to enhance the performance of Convolutional Neural Networks (CNN) in image classification tasks, particularly with breast cancer image datasets. The architectures in focus were VGG-16, ResNet50, and Inception V4, three prevalent CNN models with proven efficiency in image recognition tasks. Coupling these CNN models with AdaBoost led to notable performance improvements in individual tests. The study further pushed the envelope by constructing an ensemble model that combined all three CNN models. This ensemble, with AdaBoost, demonstrated impressive performance across various datasets. With precision and recall scores exceeding 0.94, an F1-Score of 0.96, and an overall accuracy of 0.95 to 0.99. The significant performance boost can be attributed to the richer feature space generated by the ensemble of multiple CNN models and the iterative refinement of predictions provided by the AdaBoost algorithm. Despite the ensemble model's complexity and increased computational demand, the results provide a compelling justification for its use. Further research could delve into optimizing such ensemble models, exploring other ensemble strategies, or testing the models on diverse datasets and tasks beyond image classification.

Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets

Early diagnosis plays a key role in prevention and treatment of skin cancer. Several machine learning techniques for accurate detection of skin cancer from medical images have been reported. Many of these techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. However, the classification accuracy of these models still tends to be severely limited by the scarcity of representative images from malignant tumours. We propose a novel ensemble-based convolutional neural network (CNN) architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with auxiliary data in the form of metadata associated with the input images, are combined using a meta-learner. The proposed approach improves the model’s ability to handle limited and imbalanced data. We demonstrate the benefits of the proposed technique using a dataset with 33,126 dermoscopic images from 2056 patients. We evaluate the performance of the proposed technique in terms of the F1-measure, area under the ROC curve (AUC-ROC), and area under the PR-curve (AUC-PR), and compare it with that of seven different benchmark methods, including two recent CNN-based techniques. The proposed technique compares favourably in terms of all the evaluation metrics.

Comparison of automatic liver volumetry performance using different types of magnetic resonance images

Measurements of liver volume from MR images can be valuable for both clinical and research applications. Automated methods using convolutional neural networks have been used successfully for this using a variety of different MR image types as input. In this work, we sought to determine which types of magnetic resonance images give the best performance when used to train convolutional neural networks for liver segmentation and volumetry.Abdominal MRI scans were performed at 3 Tesla on 42 adolescents with obesity. Scans included Dixon imaging (giving water, fat, and T2* images) and low-resolution T2-weighted scout images. Multiple convolutional neural network models using a 3D U-Net architecture were trained with different input images. Whole-liver manual segmentations were used for reference. Segmentation performance was measured using the Dice similarity coefficient (DSC) and 95% Hausdorff distance. Liver volume accuracy was evaluated using bias, precision, intraclass correlation coefficient, normalized root mean square error (NRMSE), and Bland-Altman analyses.The models trained using both water and fat images performed best, giving DSC = 0.94 and NRMSE = 4.2%. Models trained without the water image as input all performed worse, including in participants with elevated liver fat. Models using the T2-weighted scout images underperformed the Dixon-based models, but provided acceptable performance (DSC ≥ 0.92, NMRSE ≤6.6%) for use in longitudinal pediatric obesity interventions. The model using Dixon water and fat images as input gave the best performance, with results comparable to inter-reader variability and state-of-the-art methods.

A Deep Learning Hierarchical Ensemble for Remote Sensing Image Classification

Artificial intelligence, which has recently emerged with the rapid development of information technology, is drawing attention as a tool for solving various problems demanded by society and industry. In particular, convolutional neural networks (CNNs), a type of deep learning technology, are highlighted in computer vision fields, such as image classification and recognition and object tracking. Training these CNN models requires a large amount of data, and a lack of data can lead to performance degradation problems due to overfitting. As CNN architecture development and optimization studies become active, ensemble techniques have emerged to perform image classification by combining features extracted from multiple CNN models. In this study, data augmentation and contour image extraction were performed to overcome the data shortage problem. In addition, we propose a hierarchical ensemble technique to achieve high image classification accuracy, even if trained from a small amount of data. First, we trained the UC-Merced land use dataset and the contour images for each image on pretrained VGGNet, GoogLeNet, ResNet, DenseNet, and EfficientNet. We then apply a hierarchical ensemble technique to the number of cases in which each model can be deployed. These experiments were performed in cases where the proportion of training datasets was 30%, 50%, and 70%, resulting in a performance improvement of up to 4.68% compared to the average accuracy of the entire model.

VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks.

The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications. The ability to understand and compare various CNN models available is thus essential. The conventional approach with visualizing each model's quantitative features, such as classification accuracy and computational complexity, is not sufficient for a deeper understanding and comparison of the behaviors of different models. Moreover, most of the existing tools for assessing CNN behaviors only support comparison between two models and lack the flexibility of customizing the analysis tasks according to user needs. This paper presents a visual analytics system, VAC-CNN (Visual Analytics for Comparing CNNs), that supports the in-depth inspection of a single CNN model as well as comparative studies of two or more models. The ability to compare a larger number of (e.g., tens of) models especially distinguishes our system from previous ones. With a carefully designed model visualization and explaining support, VAC-CNN facilitates a highly interactive workflow that promptly presents both quantitative and qualitative information at each analysis stage. We demonstrate VAC-CNN's effectiveness for assisting novice ML practitioners in evaluating and comparing multiple CNN models through two use cases and one preliminary evaluation study using the image classification tasks on the ImageNet dataset.

A Palm Vein Recognition Approach by Multiple Convolutional Neural Network Models

A palm vein recognition system is proposed in this paper. The efficiency of three convolutional neural network models (VGG16, VGG19 and AlexNet) in palm vein biometrics is compared and then this study proposes to fuse them with Decision-Level Fusion. These models employ the use of high number of filters during training which leads to very high computation time, therefore, the filters are reduced in this study to drastically reduce computation time while maintaining the efficiency of the models. The proposed method is tested on three datasets secured from FYO, PUT and VERA databases. The proposed system significantly increases the accuracy of the system in comparison with the individual models and achieves 99.06 %, 99.83 % and 99.26 % on FYO, PUT and VERA datasets, respectively.

COVID-19 prediction through X-ray images using various layers in convolutional neural network

PurposeThe purpose of this paper is to focus on the prediction of Coronavirus 2019 (COVID-19) using X-ray image.Design/methodology/approachThis study proposed convolutional neural network (CNN) approach to predict COVID-19.FindingsPrediction of COVID-19 using CNN.Originality/valueThe work has implemented multiple CNN models to classify chest X-ray of affected patients by using their chest scans. According to three models, the ResNet-50 is advantageous because of its high service reliability.