CA-CNN Deep Learning Model for Image Classification

Remote-sensing images comprise massive amount of spatial and semantic data that can be employed for several applications. Presently, deep learning (DL) models for RS image processing become a familiar research area. Due to the advancements of recent satellite imaging sensors , the issue of huge amount of data processing becomes a challenging problem. To accomplish this, deep transfer learning (DTL) models are developed to resolve the semantic gap among various datasets This study develops a new DTL-based fusion model for environmental remote-sensing image classification, called DTLF-ERSIC technique. The proposed technique focuses on the design of fusion model to combine multiple feature vectors and thereby attains maximum classification performance. The DTLF-ERSIC technique incorporates the entropy-based fusion of three feature extraction techniques, namely, Discrete Local Binary Pattern (DLBP), Residual Network (ResNet50), and EfficientNet models. Besides, a rain optimization algorithm (ROA) with fuzzy rule-based classifier (FRC) is applied to predict the class labels of the test RS images and shows the novelty of the work. A comprehensive experimental analysis of the DTLF-ERSIC technique takes place on benchmark dataset and examined the results in terms of different performance measures. The simulation results reported the supremacy of the DTLF-ERSIC technique over the recent state-of-art techniques.

The aim of this study was to distinguish canine lymphoma from other diseases,particularly reactive lymphoid hyperplasia (RLH), based on fine needle aspiration (FNA)images. We developed four deep learning models based on Vision Transformer (ViT) andInception-v3, which were pre-trained image classification models. The two models out offour were ViT and Inception-v3, and the remained were the two types of combination, i.e.,ensemble learning models, of ViT and Inception-v3; the mean of class probabilities of ViTand Inception-v3 (Ensemble model A; MEAN) and the maximum probabilities of ViT andInception-v3 (Ensemble model B; MAX). A total of 2,290 FNA images of canine lymphoma and871 FNA images of RLH were analyzed. The FNA images were obtained from the twenty-fiveslides of fourteen lymphoma cases and eight slides of seven RLH cases in two hospitals.Three types of training and test datasets were prepared from the above image datasets forfair evaluation of the models. Three deep learning-based image classification models(Inception-v3 and the two ensemble models) attained high performance of >80% accuracy,recall and area under the curve (AUC) values for all three datasets. ViT did not archivehigh performance, except the precision (>0.85). This study is an example of showingpotentials of deep learning models through image classification problem in caninelymphoma.

In the task of music genre classification, feature extraction and classifier modeling are the two key parts that directly affect the classification accuracy. In the traditional classification method, the feature extraction and classification processes are designed separately. First, the features are extracted manually from the original music signal, and then a reasonable classifier is selected to build a model and classify the extracted features. Although the traditional methods have achieved good results in many classification tasks, the feature extraction process is complex and difficult to achieve, and the features required for different classification tasks need to be specially designed, and the extracted features lack of universality. With the successful application and continuous development of deep learning models in other fields, more and more studies begin to use music spectra as the input of deep learning models to classify music genres. However, so far, the accuracy of existing classification methods based on deep learning is not ideal, so this paper mainly studies a classification method based on deep learning to improve the classification accuracy of music genre classification model. In this paper, a parallel structured deep attention classification model is proposed. By training BRNN, it can automatically learn music features from samples. The linear attention model calculates the attention probability distribution on this feature according to the learned features, and reassigns it to the feature representation. Finally, the classification is realized according to the eigenvectors with different weights. Besides the linear attention model with simple structure, a CNN attention model with stronger learning ability is also designed. In order to verify the feasibility and validity of the model, validation experiments were conducted on two standard datasets, GTZAN and Extended Ballroom. The experimental results show that the classification model based on deep parallel attention mechanism has good classification robustness, and the accuracy of classification based on BRNN and parallel CNN attention model reaches 92.7% on Extended Ballroom data set, which is better than the existing classification method based on deep learning. The validity and feasibility of the classification model are proved.

To review studies on deep learning (DL) models for classification, detection, and segmentation of rib fractures in CT data, to determine their risk of bias (ROB), and to analyse the performance of acute rib fracture detection models. Research articles written in English were retrieved from PubMed, Embase, and Web of Science in April 2023. A study was only included if a DL model was used to classify, detect, or segment rib fractures, and only if the model was trained with CT data from humans. For the ROB assessment, the Quality Assessment of Diagnostic Accuracy Studies tool was used. The performance of acute rib fracture detection models was meta-analysed with forest plots. A total of 27 studies were selected. About 75% of the studies have ROB by not reporting the patient selection criteria, including control patients or using 5-mm slice thickness CT scans. The sensitivity, precision, and F1-score of the subgroup of low ROB studies were 89.60% (95%CI, 86.31%-92.90%), 84.89% (95%CI, 81.59%-88.18%), and 86.66% (95%CI, 84.62%-88.71%), respectively. The ROB subgroup differences test for the F1-score led to a p-value below 0.1. ROB in studies mostly stems from an inappropriate patient and data selection. The studies with low ROB have better F1-score in acute rib fracture detection using DL models. This systematic review will be a reference to the taxonomy of the current status of rib fracture detection with DL models, and upcoming studies will benefit from our data extraction, our ROB assessment, and our meta-analysis.

Most popular image classification methods mainly focus on classification ability rather than recognizing new things. However, human lay emphasis on cognition first and then classification, which is closely related to human memory system. Though many memory models have been proposed, they are studied in word list whereas the reports about natural images are still limited. This paper proposes a memory model for image recognition and classification based on convolutional neural network and Bayesian decision. First the image feature is extracted by convolutional neural network and stored in binary form. Then the representation, storage and retrieval processes of visual images are modeled. The test image feature vector is matched in parallel to the studied image vectors, and the likelihood values are calculated. Finally, the odd that the test image belongs to a new class is computed based on all likelihood values. If the odd value is greater than a certain threshold, the test image is regarded as new; otherwise, the Bayesian decision rule for image classification is performed. Experimental results on Caltech-101 and Caltech-256 datasets show that the proposed method can perform well in image recognition and classification tasks. And the hit probability of the method is higher than two typical methods, SRC and ELM, at present while the false alarm rate is far lower than them.

Deep learning-based image classification and quantification models for tablet sticking.

The significant role of emotion in human daily interaction cannot be over-emphasized, however, the pressing demand for a cutting-edge and highly efficient model for the classification of speech emotion in effective computing has remained a challenging task. Researchers have proposed several approaches for speech emotion classification (SEC) in recent times, but the lingering challenge of the insufficient dataset, which has been limiting the performances of these approaches, is still of major concern. Therefore, this work proposes a deep transfer learning model, a technique that has been yielding tremendous and state-of-the-art results in computer vision, for SEC. Our approach used a pre-trained and optimized model of Visual Geometry Group (VGGNet) convolutional neural network architecture with appropriate fine-tuning for optimal performance. The speech signal is converted to a mel-Spectrogram image suitable for deep learning model input (224\(\,\times \,\)244 x 3) using filterbanks and Fast Fourier transform (FFT) on the speech samples. Multi-layer perceptron (MLP) algorithm is adopted as a classifier after feature extraction is carried out by the deep learning model. Speech pre-processing was carried out on Toronto English Speech Set (TESS) speech emotional corpus used for the study to prevent the low performance of our proposed model. The result of our experiment after evaluation using the TESS dataset shows an improved result in SEC with an accuracy rate of 96.1% and specificity of 97.4%.KeywordsDeep learningSpeech emotionClassificationDeep convolutional neural network

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low invasiveness. However, thyroid images obtained from current ultrasound tests often have low resolution and are plagued by significant noise interference. Regional differences in medical conditions and varying levels of physician experience can impact the accuracy and efficiency of diagnostic results. With the advancement of deep learning technology, deep learning models are used to identify whether a nodule in a thyroid ultrasound image is benign or malignant. This helps to close the gap between doctors’ experience and equipment differences, improving the accuracy of the initial diagnosis of thyroid nodules. To cope with the problem that thyroid ultrasound images contain complex background and noise as well as poorly defined local features. in this paper, we first construct an improved ResNet50 classification model that uses a two-branch input and incorporates a global attention lightening module. This model is used to improve the accuracy of benign and malignant nodule classification in thyroid ultrasound images and to reduce the computational effort due to the two-branch structure.We constructed a U-net segmentation model incorporating our proposed ACR module, which uses hollow convolution with different dilation rates to capture multi-scale contextual information for feature extraction of nodules in thyroid ultrasound images and uses the results of the segmentation task as an auxiliary branch for the classification task to guide the classification model to focus on the lesion region more efficiently in the case of weak local features. The classification model is guided to focus on the lesion region more efficiently, and the classification and segmentation sub-networks are respectively improved specifically for this study, which is used to improve the accuracy of classifying the benign and malignant nature of the nodules in thyroid ultrasound images. The experimental results show that the four evaluation metrics of accuracy, precision, recall, and f1 of the improved model are 96.01%, 93.3%, 98.8%, and 96.0%, respectively. The improvements were 5.7%, 1.6%, 13.1%, and 7.4%, respectively, compared with the baseline classification model.

Optical Coherence Tomography (OCT) is a standard tool for cross-sectional imaging of retinal tissues. A large number of OCT scans are performed yearly, and ophthalmologists examine the OCT scans to detect eye diseases. Computer-assisted diagnosis (CAD) or decision support system is needed to screen OCT scans for disease detection. Earlier machine learning models were based on finding the most discriminative features by domain experts and then building the machine learning classifier. But recent research shows deep learning models are more suitable and give promising results. This paper summarizes the major deep learning approaches for OCT image classification for the detection of the most common retinal diseases, namely, AMD (Age-related Macular degeneration), DME (Diabetic Macular Edema), and CNV (Choroidal Neovascularization). We have discussed the advantages and challenges of deep learning models for OCT image classification, which can give directions for future research.

Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce-compared to other medical imaging data-we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model's performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.

Deep Neural Network (DNN) models are widely used for image classification. While they offer high performance in terms of accuracy, researchers are concerned about if these models inappropriately make inferences using features irrelevant to the target object in a given image. To address this concern, we propose a metamorphic testing approach that assesses if a given inference is made based on irrelevant features. Specifically, we propose two metamorphic relations (MRs) to detect such unreliable inferences. These relations expect (a) the classification results with different labels or the same labels but less certainty from models after corrupting the relevant features of images, and (b) the classification results with the same labels after corrupting irrelevant features. The inferences that violate the metamorphic relations are regarded as unreliable inferences. Our evaluation demonstrated that our approach can effectively identify unreliable inferences for single-label classification models with an average precision of 64.1% and 96.4% for the two MRs, respectively. As for multi-label classification models, the corresponding precision for MR-1 and MR-2 is 78.2% and 86.5%, respectively. Further, we conducted an empirical study to understand the problem of unreliable inferences in practice. Specifically, we applied our approach to 18 pre-trained single-label image classification models and 3 multi-label classification models, and then examined their inferences on the ImageNet and COCO datasets. We found that unreliable inferences are pervasive. Specifically, for each model, more than thousands of correct classifications are actually made using irrelevant features. Next, we investigated the effect of such pervasive unreliable inferences, and found that they can cause significant degradation of a model’s overall accuracy. After including these unreliable inferences from the test set, the model’s accuracy can be significantly changed. Therefore, we recommend that developers should pay more attention to these unreliable inferences during the model evaluations. We also explored the correlation between model accuracy and the size of unreliable inferences. We found the inferences of the input with smaller objects are easier to be unreliable. Lastly, we found that the current model training methodologies can guide the models to learn object-relevant features to certain extent, but may not necessarily prevent the model from making unreliable inferences. We encourage the community to propose more effective training methodologies to address this issue.

Deep learning-based image classification for online multi-coal and multi-class sorting

As monkeypox is spreading rapidly, the incidence of monkeypox has been increasing in recent times. Therefore, it is very important to detect and diagnose this disease to get effective treatment planning. The prominent aim of this paper is to design an effective monkeypox detection and classification model by utilizing deep learning and classification models. In this study, a novel Adaptive Generative 3D VNet model is presented to effectively classify the monkeypox lesions. Different data augmentation approaches, deep learning, and adaptive fusion are integrated into the proposed model to attain better results in disease classification. The major objective of the proposed model is to mitigate the challenges of limited labeled data by generating synthetic augmented images and combining them with real images for robust classification. The two major components of the proposed system are the Adaptive Generative Network and the 3D VNet. Additional training models are generated by the adaptive generative network through augmentation approaches including cropping, rotation, and flipping, thereby increasing the diversity of the dataset. The 3D VNet processes these images in a volumetric manner to capture spatial relationships within the lesions, improving classification accuracy. The fusion layer then adaptively combines the predictions from the real and augmented data to optimize the overall effectiveness of a model. Key performance metrics including accuracy, precision, sensitivity, specificity, Jaccard Index, Hausdorff distance, and Dice Similarity Coefficient are used to compute the effectiveness of a model. The findings show that the Adaptive Generative 3D VNet model outperforms traditional 2D models by significantly improving the classification accuracy and robustness, especially in the presence of limited labeled data. Therefore, the simulation results demonstrate that the proposed model achieves high accuracy and precision of 98.8% and 98.5%, respectively based on the Monkeypox Skin Lesion Dataset.

The development of brain-computer interface (BCI) applications has recently piqued the interest of researchers because it can help physically handicapped people to communicate with their brain electroencephalogram (EEG) signal. The automatic classification of mental workload tasks from multi-channel EEG signal analysis is critical for BCI applications. In this paper, we propose an end-to-end deep learning (DL) model for the classification of the mental arithmetic task (MAT) from multi-channel EEG signals. As an end-to-end DL model, a residual-based temporal attention network (RTA-Net) is developed to achieve optimal performance for MAT classification. We have mainly considered two MAT: before mental arithmetic calculation and during mental arithmetic calculation. The RTA-Net model is validated on a freely available MAT-based EEG dataset. The results show that our proposed model yield the best performance with classification accuracy: 99.32%, F1-score: 99.20%, and Cohen’s Kappa: 98.15%, which defeat the performance of all existing methods for MAT classification. For real-world applications, our automated MAT system is ready to be tested with additional datasets.