Hybrid deep learning framework for robust time-series classification: Integrating inception modules with residual networks

This study proposes a hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model for the accurate diagnosis of anemia types, leveraging the strengths of both architectures in capturing spatial and temporal patterns in Complete Blood Count (CBC) data. The research involves the development and evaluation of various models of single-architecture deep learning (DL) models, specifically Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Fully Convolutional Network (FCN). The models are trained and validated using stratified k-fold cross-validation to ensure robust performance. Key metrics such as test accuracy are utilized to provide a comprehensive assessment of each model's performance. The hybrid CNN-RNN model achieved the highest test accuracy of 90.27%, surpassing the CNN (89.88%), FCN (85.60%), MLP (79.77%), and RNN (73.54%) models. The hybrid model also demonstrated superior performance in cross-validation, with an accuracy of 87.31% ± 1.77%. Comparative analysis highlights the hybrid model's advantages over single-architecture DL models, particularly in handling imbalanced data and providing reliable classifications across all anemia types. The results underscore the potential of advanced DL architectures in medical diagnostics and suggest pathways for further refinements, such as incorporating attention mechanisms or additional feature engineering, to enhance model performance. This study contributes to the growing body of knowledge on AI-driven medical diagnostics and presents a viable tool for clinical decision support in anemia diagnosis

Alarm fatigue is a significant issue in healthcare where clinicians become desensitized to an alert from a monitored patient, primarily due to the high frequency of false alarms. Importantly, electrocardiogram (ECG) changes indicative of myocardial ischemia (M-Isch) are not reliably detected by current algorithms. The clinical use of deep learning (DL) is becoming more prevalent, with a growing emphasis on detecting subtle changes in various biosignals that may be otherwise overlooked due to human error. Here, we investigate the robustness of various DL architectures for the detection of M-Isch. We hypothesized that DL models will be able to achieve a high level of specificity and sensitivity (> 90%) when classifying ECG recordings indicative of M-Isch and normal sinus rhythm. This study leverages the advanced capabilities of timeseriesAI (tsai), a library of DL frameworks specializing in time series decoding, applied for the first time to M-Isch classification. Additionally, the models were trained on the European ST-T database (available from PhysioNet), which consists of ECGs annotated by cardiologists for various ST-T changes associated with M-Isch. The dataset also utilizes ambulatory ECGs, which is not only clinically relevant, but increases the decoding difficulty, as the presence of noise and artifacts inherent in ambulatory recordings necessitates robust generalizability. M-Isch is often transient, resulting in a limited number of beats with detectable ST-T changes within the given dataset. To address class imbalance, the data was up sampled using various data augmentation techniques. The dataset was divided into a training set, validation set, and test set, with a split ratio of 0.70, 0.20 and 0.10 respectively, which was determined to provide sufficient data for training, while allowing for adequate validation and training. To mitigate data leakage, the data was stratified to ensure balanced class representation in each batch, with sufficient temporal separation between training, validation, and test sets. Additionally, each batch was standardized using z-score normalization to ensure consistent input distributions across batches during training. The performances of six deep learning models—Fully Convolutional Network (FCN), Residual Network (ResNet), Temporal Convolutional Network (TCN), OmniScale Convolutional Neural Network (CNN), Residual Convolutional Neural Network (ResCNN) and InceptionTime— were compared. Initial training was constrained to binary classification between either single-lead sinus rhythm or ST-elevation (>0.2 mV) M-Isch. Individual beats, defined by 276 milliseconds (ms) before and 500 ms after the R wave respectively, were used to train and test the models from a single subject within the database. The results show that the ensemble-based architectures outperformed all other architectures, with high sensitivity, specificity and accuracy, precision and F1 scores (InceptionTime: 91.75%, 92.20%, 93.01%, 90.86% and 0.9371, respectively; ResCNN: 89.64%, 91.84%, 90.25%, 87.52% and 0.9187, respectively). These findings suggest that DL has the potential to improve the sensitivity and specificity of ambulatory M-Isch detection from a general performance level of sensitivity ranging from 50% to 72% and specificity between 69% and 90%. This could conceivably lead to earlier identification of ischemia and facilitating important timely interventions in remote monitoring of ECGs that could prevent adverse cardiac events associated with M-Isch outside of traditional clinical settings. This study was supported by: NIH (R01 NS131493) This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

The information hidden in an image is worth more than a thousand words. Proper analysis of a medical image can help in timely detection and diagnose of a disease which increases the rate of survival of cancer patients. Analysis of images manually is subjective and time consuming. On the other hand, automated analysis of a medical image has a lot of challenges due to the architecture and colors of the medical images. This paper, gives a survey on detection, classification and diagnosis of colorectal cancer and proposes a deep learning based techniques to differentiate between healthy tissues and cancerous polyps in histology images. It also compares the accuracy of three different classification frameworks namely Convolutional Neural Network (CNN), Fully Convolutional Network (FCN) and Recurrent Neural Network (RNN). It also presents the overview of the work done in this field. It first discusses basic deep learning methods and then the known techniques used for detection, classification and diagnosis of colorectal cancer followed by the comparative analysis of all the surveyed paper. Finally, it talks about the conclusion, challenges and the future scope of the progress in this field.

Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis.

For women, most common cause of death is Breast tumour and in worldwide, it is the second leading reason for cancer deaths. Due the requirement of breast cancer’s early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. There are four stages in this proposed HIRResCNN framework, namely, Pre-processing, reduction of dimensionality, segmentation and classification. From images, noises are removed using two filtering algorithms called Median and mean filtering in pre-processing stage. Then canny edge detector is used for detecting edges. Gaussian filtering is used in canny edge detector to smoothen the images. In the next dimensionality reduction stage, attributes are correlated using Principal Component Analysis (PCA) inclusive of related features. So, this huge dataset is minimized and only few variables are used for expressing it. In order to detect the breast cancer accurately, foreground and background subtraction is done in the third stage called segmentation stage. At last, for detecting and classifying breast cancer, a Hybrid Inception Recurrent Residual Convolutional Neural Network (HIRResCNN) is introduced, which integrates Harmony Search Optimization (HSO) to tune bias and weight parameters and classification accuracy is enhanced using HIRResCNN-HSO model. Strength of Recurrent Convolutional Neural Network (RCNN), Residual Network (ResNet) and Inception Network (Inception-v4), are combined in a powerful Deep Convolutional Neural Network (DCNN) model called HIRResCNN. using Mammographic Image Analysis Society (MIAS) dataset, various experiments are conduced and results are compared with other available techniques. Around 92.6% accuracy rate is produced using this proposed HIRResCNN classifier in finding breast cancer.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Deep learning networks have shown great success in several computer vision applications, but its implementation in natural land cover mapping in the context of object-based image analysis (OBIA) is rarely explored area especially in terms of the impact of training sample size on the performance comparison. In this study, two representatives of deep learning networks including fully convolutional networks (FCN) and patch-based deep convolutional neural networks (DCNN), and two conventional classifiers including random forest and support vector machine were implemented within the framework of OBIA to classify seven natural land cover types. We assessed the deep learning classifiers using different training sample sizes and compared their performance with traditional classifiers. FCN was implemented using two types of training samples to investigate its ability to utilize object surrounding information.Our results indicate that DCNN may produce inferior performance compared to conventional classifiers when the training sample size is small, but it tends to show substantially higher accuracy than the conventional classifiers when the training sample size becomes large. The results also imply that FCN is more efficient in utilizing the information in the training sample than DCNN and conventional classifiers, with higher if not similar achieved accuracy regardless of sample size. DCNN and FCN tend to show similar performance for the large sample size when the training samples used for training the FCN do not contain object surrounding label information. However, with the ability of utilizing surrounding label information, FCN always achieved much higher accuracy than all the other classification methods regardless of the number of training samples.

BackgroundEpilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed.MethodsIn this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture’s traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results.ResultsOn the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance.ConclusionThe model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG.

From center to surrounding: An interactive learning framework for hyperspectral image classification

Convolutional Neural Network (CNN) is the prevalent framework of image classification, and is also widely used in other image applications including semantic segmentation. Advantages of CNN include hierarchical extraction of patterns, effective training and a small number of parameters. Do they make CNN the ideal candidate for all image processing tasks? In a patch-based semantic image segmentation experiment of Nasopharyngeal Carcinoma (NPC), three different network structures are tested on the same input - CNN, Recurrent Neural Network (RNN) and a mixed model. Results show that RNN achieves comparable accuracy to CNN, and replacing fully- connected layers with RNN further improves performance. A 2D Graph Cut algorithm follows each network to post-process heat maps for a global optimal solution. Within the limited scope of a patch, CNN, RNN and RNN-injected CNN return similar performance metrics, but it becomes a more complex issue to design an appropriate network structure for the complete image, which may be much larger or even in higher dimension. Fully Convolutional Networks (FCNs) have also been proposed for semantic image segmentation, in which the full image goes through consecutive convolutional units for coarse-grained features, then the smaller feature maps must be up-sampled to match the original size. A multidimensional recurrent network at the pixel level or on top of CNN-processed patches will be explored next, aiming at a larger receptive field and higher training efficiency.

In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

Advancements in Deep Learning Algorithms is a comprehensive exploration of the cutting-edge developments in deep learning, a subset of artificial intelligence that has revolutionized the way machines learn from data. This book starts with the basics, introducing the reader to the fundamental concepts and terminologies of deep learning, before delving into the core algorithms that form the backbone of this field, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). It further explores advanced architectures and techniques such as attention mechanisms, deep reinforcement learning, federated learning, and autoencoders, providing a deep dive into the mechanisms that enable machines to mimic human-like learning processes. The book also addresses critical aspects of data handling and preprocessing, optimization and regularization techniques, and the practical applications of deep learning in various industries, highlighting real-world case studies. Additionally, it discusses the challenges, ethical considerations, and future implications of deploying deep learning technologies. With an eye towards recent trends and the future directions of deep learning, this book aims to equip researchers, practitioners, and enthusiasts with the knowledge to understand and leverage the potential of deep learning in solving complex problems. Keywords: Deep Learning, Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Attention Mechanisms, Deep Reinforcement Learning, Federated Learning, Autoencoders, Data Preprocessing, Optimization Techniques, Artificial Intelligence, Industry Applications, Ethical Considerations, Future Directions.

In this study, we propose a new deep learning (DL) framework, a combination of fully convolutional and recurrent neural networks, and integrate them with a weakly supervised method for 3D MRI-transrectal ultrasound (TRUS) image registration. The MR and TRUS images are often highly anisotropic in its dimensions. For instance, in 3D US images the scale of each voxel in depth is often 5~10 times larger than that in each image slice. This high anisotropy makes the common 3D isotropic kernel suffer poor generality, resulting in unsatisfactory registration results in terms of accuracy. The key idea of the paper is to explicitly leverage 3D image anisotropy through the exploitation of the intra-slice context with a fully convolutional network (FCN) and the utilization of the inter-slice context with a recurrent neural network (RNN). After the 3D hierarchical features in MRI and TRUS have been extracted, we generate the dense deformation field by aligning corresponding prostate labels for individual image pairs. Experimental results showed that our proposed FCN-RNN neural network produces a mean target registration error (TRE) of 2.77±1.40 mm, and a mean dice similarity coefficient (DSC) of 0.9.

In order to solve the problem of feature extraction of brake pad surface defects in traditional machine learning, an automatic defect detection system based on deep learning is designed. Firstly, the image acquisition system is designed and the brake pad image dataset is constructed. Then, a kind of defect detection method based on convolutional neural network (CNN) is proposed. The dataset is input into the CNN model for training, and the appropriate defect classification model of brake pad is selected for defect detection and classification. In addition, in order to further improve the performance of the detection system, a defect detection method based on full convolution network (FCN) is also proposed. Finally, the two methods mentioned above are tested. The results show that both methods are suitable for the detection of brake pad defects and can realize the recognition and classification of brake pad defects. In comparison, the method based on CNN has higher detection efficiency than the one based on FCN, but the method based on FCN has better recognition accuracy and stronger performance than the one based on CCN.