A Text Recognition Augmented Deep Learning Approach for Logo Identification

Here we investigate the application of convolutional neural networks (CNNs) to predict the survival time of patients with Glioblastoma Multiforme (GBM) brain tumor. Our dataset consists of T1-weighted high-resolution MRI images of just 68 GBM patients. We compare two analytic methods for predicting survival time. The first consists of training a small convolutional neural network (CNN) and the second uses extracted deep features from a pre-trained CNN. Our method is completely automated, except for tumor region segmentation. In addition, we utilize a snapshot ensemble approach to boost test accuracy when dealing with limited availability of medical images for CNN training purposes. Our approach achieves an accuracy of 72.06% using a trained small network and 66.18% using a pre-trained deep CNN. Our results compare favorably with the accuracy of 54.41% using histogram of oriented gradients (HOG) features and a non-neural network classifier.

Early and accurate identification of brain tumors is essential for determining effective treatment strategies and improving patient outcomes. Artificial intelligence (AI) and deep learning (DL) techniques have shown promise in automating diagnostic tasks based on magnetic resonance imaging (MRI). This study evaluates the performance of four pre-trained deep convolutional neural network (CNN) architectures for the automatic multi-class classification of brain tumors into four categories: Glioma, Meningioma, Pituitary, and No Tumor. The proposed approach utilizes the publicly accessible Brain Tumor MRI Msoud dataset, consisting of 7023 images, with 5712 provided for training and 1311 for testing. To assess the impact of data availability, subsets containing 25%, 50%, 75%, and 100% of the training data were used. A stratified five-fold cross-validation technique was applied. The CNN architectures evaluated include DeiT3_base_patch16_224, Xception41, Inception_v4, and Swin_Tiny_Patch4_Window7_224, all fine-tuned using transfer learning. The training pipeline incorporated advanced preprocessing and image data augmentation techniques to enhance robustness and mitigate overfitting. Among the models tested, Swin_Tiny_Patch4_Window7_224 achieved the highest classification Accuracy of 99.24% on the test set using 75% of the training data. This model demonstrated superior generalization across all tumor classes and effectively addressed class imbalance issues. Furthermore, we deployed and benchmarked the best-performing DL model on embedded AI platforms (Jetson AGX Xavier and Orin Nano), demonstrating their capability for real-time inference and highlighting their feasibility for edge-based clinical deployment. The results highlight the strong potential of pre-trained deep CNN and transformer-based architectures in medical image analysis. The proposed approach provides a scalable and energy-efficient solution for automated brain tumor diagnosis, facilitating the integration of AI into clinical workflows.

TV logo is one of the important symbols of TV station. Nowadays TV logo recognition has been widely used in various fields, such as video analysis, video surveillance, video management and so on. According to TV logo's characteristics, a TV logo detection and recognition algorithm based on SURF feature and bag-of-visual-words model is proposed in this paper. First, obtain binary logo images by using the automatic image segmentation method based on Otsu in the region of interest, and then execute AND operation with original images, thereby completing the logo extraction. Second, extract logo images' SURF features, and then use k-means clustering algorithm to construct a visual dictionary, and ultimately use support vector machine (SVM) to complete logo recognition. Experimental results show that the proposed method has high recognition accuracy, strong anti-interference ability and good practicality.

Deep learning based methods have recently been successfully explored in hyperspectral image classification field. However, training a deep learning model still requires a large number of labeled samples, which is usually impractical in hyperspectral images. In this paper, a simple but effective feature extraction method is proposed for hyperspectral image classification. Specifically, a pretrained deep convolutional neural network based on the ImageNet dataset is used to extract spatial features of a hyperspectral image. Recently, it is easy to obtain a pretrained convolutional neural network on the Internet. Note that the pretrained models are trained by using the ImageNet dataset. This means that the proposed method does not need labeled hyperspectral samples to train the deep model. Therefore, the proposed method alleviates the problem of lacking labeled samples and avoids the artificial design of feature extraction rules. Finally, the extracted features are stacked with spectral features as the input of a support vector machine classifier. The proposed method is conducted on three widely used hyperspectral image datasets. The experimental results demonstrate that the proposed method could outperform the conventional feature extraction methods and deep learning based methods.

Automated integrated system for stained neuron detection: An end-to-end framework with a high negative predictive rate

In order to solve the problem of automation of cable number identification in the process of cable assembly of large equipment, we proposed a cable number detection and recognition method based on image Maximally Stable Extremal Regions (MSER) and convolutional neural network (CNN). We used image processing algorithms combined with deep learning to achieve end-to-end identification of cable number images. In view of the uncertain arrangement order and scattered distribution of numbers on the cable, the MSER algorithm was improved by image processing in order to extract the area containing numbers more accurately. The number identification problem in the case of lack of sample database is solved by the migration-trained CNN network. In addition, the recognition effect of the method under different environmental conditions has been studied. Through experimental research, the optimized MSER and CNN algorithms can better complete the cable number detection and identification tasks.

Feature fusion methods have been demonstrated to be effective for many computer vision based applications. These methods generally use multiple hand-crafted features. However, in recent days, features extracted through transfer leaning procedures have been proved to be robust than the hand-crafted features in myriad applications, such as object classification and recognition. The transfer learning is a highly appreciated strategy in deep convolutional neural networks (DCNNs) due to its multifaceted benefits. It heartens us to explore the effect of fusing multiple transfer learning features of different DCNN architectures. Thus, in this work, we extract features of image statistics by exploiting three different pre-trained DCNNs through transfer learning. Then, we transform the features into a generalized subspace through a recently introduced Autoencoder network and fuse them to form intra-class invariant feature vector that is used to train a multi-class Support Vector Machine (SVM). The experimental results on various datasets, including object and action image statistics show that the fusion of multiple transfer learning features improves classification accuracy as compared to fusion of multiple hand-crafted features and usage of single component transfer learning features.

We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The regularization parameters in LS-SVM is estimated automatically without grid search and cross-validation by maximizing evidence, which is a useful measure to select the best performing CNN out of multiple candidates for transfer learning, the evidence is optimized efficiently by employing Aitken's delta-squared process, which accelerates convergence of fixed point update. The proposed Bayesian evidence framework also provides a good solution to identify the best ensemble of heterogeneous CNNs through a greedy algorithm. Our Bayesian evidence framework for transfer learning is tested on 12 visual recognition datasets and illustrates the state-of-the-art performance consistently in terms of prediction accuracy and modeling efficiency.

A multi-model data-fusion based deep transfer learning for improved remaining useful life estimation for IIOT based systems

Logo recognition is one of the applied aspects of graphic recognition domain. In most of document images, some diverse conditions such as noise existence, occlusion, and different scale/orientation may affect logo recognition process. In this paper, a novel approach based on spatial and structural features of logo images is proposed to overcome those problems. After normalization step which eliminates the sensitivity of the logo recognition process to different scale/orientation, a novel feature is extracted based on horizontal and vertical histograms of the logo image. Finally, KNN classifier is used to recognize logo images. Our proposed algorithm is evaluated on a standard logo dataset of Maryland University. The experimental results show the robustness of the proposed approach in the logo recognition.

This study focuses on the detection of COVID-19 using chest X-ray images. With the rapid global spread of the highly contagious disease, early and accurate detection is crucial to prevent further transmission. We propose a multi-view approach that leverages different deep-learning methods to detect COVID-19 based on chest X-ray images. The framework presented in this study aims to capture both complementary and correlative information from multiple views. By using pre-trained deep convolutional neural network (CNN) models such as ResNet50 and VGG16 to extract deep features from the X-ray images. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms are then employed to classify the X-ray images based on the learned feature representations. The single-view models using VGG16 and ResNet50 with SVM achieved accuracy scores of 97% and 98% respectively. Similarly, using KNN, the single-view models achieved accuracy scores of 89% and 95%. Recognizing the potential of multi-view learning to improve generalization performance. We utilized early fusion to integrate the outputs of the pre-trained models and fed them into SVM and KNN classifiers. The multi-view model achieved accuracy scores of 98% and 92% respectively. The experimental results demonstrate that the multi-view deep learning methods outperformed the single-view deep models in detecting COVID-19 from chest X-ray images. The findings of this study have practical implications as they can assist experts in early diagnosis of COVID-19 cases, highlighting the effectiveness of the proposed methods.

BackgroundThe images of different flower species had small inter-class variations across different classes as well as large intra-class variations within a class. Flower classification techniques are mainly based on the features of color, shape and texture, however, the procedure always involves too many heuristics as well as manual labor to tweak parameters, which often leads to datasets with poor qualitative and quantitative measures. The current study proposed a deep architecture of convolutional neural network (CNN) for the purposes of improving the accuracy of identifying the white flowers of Fragaria × ananassa from other three wild flower species of Androsace umbellata (Lour.) Merr., Bidens pilosa L. and Trifolium repens L. in fields.ResultsThe explored CNN architecture consisted of eightfolds of learnable weights including 5 convolutional layers and 3 fully connected layers, which received a true color 227 × 227 pixels flower image as its input. The developed CNN detector was able to classify the instances of flowers at overall average accuracies of 99.2 and 95.0% in the training and test procedure, respectively. The state-of-the-art CNN model was compared with the classical models of the scale-invariant feature transform (SIFT) features and the pyramid histogram of orientated gradient (PHOG) features combined with the multi-class support vector machine (SVM) algorithm. The proposed model turned out to be much more accurate than the traditional models of SIFT + SVM at overall average accuracies of 82.9 and 55.6% in the training and test procedure and PHOG + SVM at overall average accuracies of 78.3 and 63.1%, respectively.ConclusionsThe proposed state-of-the-art CNN method demonstrates that artificial intelligence is capable of precise classification of the white flower images, whose accuracy is comparable to traditional algorithms. The presented algorithm can be further used for the discrimination of white wild flowers in fields.

Mathematical expression detection in scientific documents is a prerequisite step for developing a mathematical retrieval system that has attracted many researches recently. In the detecting process, a challenging issue is the detection of variable. The similar properties of variable and narrative text cause many errors in the detection in existing approaches. In the paper, pre-trained deep Convolutional Neural Networks (CNN) are employed and optimized to automatically extract visual features of images and Support Vector Machine (SVM) is used to improve the accuracy of the detection. The accuracy of 99.5% is achieved for the detection of variable in inline expressions in document images in public benchmark datasets. The performance comparison with traditional methods demonstrates the effectiveness of the proposed method.

Plant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recently been used to identify and diagnose diseased plants from digital images to help automate plant disease diagnosis and help non-experts identify diseased plants. While many deep learning applications have been used to identify diseased plants and aims to increase the detection rate, the limitation of the large parameter size in the models persist. In this paper, an end-to-end deep learning model is developed to identify healthy and unhealthy corn plant leaves while taking into consideration the number of parameters of the model. The proposed model utilizes two pre-trained convolutional neural networks (CNNs), EfficientNetB0, and DenseNet121, to extract deep features from the corn plant images.The deep features extracted from each CNN are then fused using the concatenation technique to produce a more complex feature set from which the model can learn better about the dataset. In this paper, data augmentation techniques were used to add variations to the images in the dataset used to train the model, increasing the variety and number of the images and enabling the model to learn more complex cases of the data. The obtained result of this work is compared with other pre-trained CNN models, namely ResNet152 and InceptionV3, which have a larger number of parameters than the proposed model and require more processing power. The proposed model is able to achieve a classification accuracy of 98.56% which shows the superiority of the proposed model over ResNet152 and InceptionV3 that achieved a classification accuracy of 98.37% and 96.26% respectively.

The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CNNs). Our results demonstrated that the deep CNN models (e.g., ResNeXt) trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error (MAE) of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC (area under the receiver operating characteristic curve) of 88%. ResNeXt model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a MAE of 9.06 kg. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pretrained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for noninvasive, convenient, and inexpensive patient health monitoring and diagnosis.