Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.

Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review systematically examines the evolution of the You Only Look Once (YOLO) object detection framework from YOLO-v1 to YOLO-v11, emphasizing architectural advancements such as attention-based feature refinement and Transformer integration and their impact on fabric defect detection. Unlike prior studies focusing on specific YOLO variants, this work comprehensively compares the entire YOLO family, highlighting key innovations and their practical implications. We also discuss the challenges, including dataset limitations, domain generalization, and computational constraints, proposing future solutions such as synthetic data generation, federated learning, and edge AI deployment. By bridging the gap between academic advancements and industrial applications, this review is a practical guide for selecting and optimizing YOLO models for fabric inspection, paving the way for intelligent quality control systems.

In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.

The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It is highly relevant in industries like textiles, where speed and accuracy are critical. In the textile industry, accurate fabric type detection and classification are essential for improving quality control, optimizing inventory management, and enhancing customer satisfaction. This paper proposes a new approach using the YOLOv10 model, which offers enhanced detection accuracy, processing speed, and detection on the torn path of each type of fabric. We developed and utilized a specialized, annotated dataset featuring diverse textile samples, including cotton, hanbok, cotton yarn-dyed, and cotton blend plain fabrics, to detect the torn path in fabric. The YOLOv10 model was selected for its superior performance, leveraging advancements in deep learning architecture and applying data augmentation techniques to improve adaptability and generalization to the various textile patterns and textures. Through comprehensive experiments, we demonstrate the effectiveness of YOLOv10, which achieved an accuracy of 85.6% and outperformed previous YOLO variants in both precision and processing speed. Specifically, YOLOv10 showed a 2.4% improvement over YOLOv9, 1.8% over YOLOv8, 6.8% over YOLOv7, 5.6% over YOLOv6, and 6.2% over YOLOv5. These results underscore the significant potential of YOLOv10 in automating fabric detection processes, thereby enhancing operational efficiency and productivity in textile manufacturing and retail.