Advanced Deep Learning Models Research Articles

Classification Insights into Brain MRI Classification: Techniques, Interpretability, and Future

This research paper comprehensively analyses various techniques for classifying brain MRI images. Through an extensive literature survey, the study explores and evaluates the effectiveness of different methodologies, ranging from traditional machine learning approaches to advanced deep learning models, in the context of brain tumour classification. The review critically examines feature extraction, selection methods, and classification algorithms, focusing on their performance in enhancing diagnostic accuracy. The synthesis of existing literature provides valuable insights into the current landscape of brain MRI classification techniques, shedding light on their strengths, limitations, and potential areas for future research.

Advancing Tuberculosis Detection in Chest X-rays: A YOLOv7-Based Approach

In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesions and their detection, respectively. For this study, we experimented on the TBX11K dataset, which is a publicly available dataset. In our experiment, we employed class weights and data augmentation techniques to address the data imbalance present in the dataset. This technique shows a promising improvement in the model’s performance and thus better generalization. In addition, it also shows that the developed model achieved promising results with a mAP (mean average precision) of 0.587, addressing class imbalance and yielding a robust performance for both obsolete pulmonary TB and active TB detection. Thus, our CAD system, rooted in state-of-the-art deep-learning and computer vision methodologies, not only advances diagnostic accuracy but also contributes to the mitigation of TB transmission risks. The substantial improvement in the model’s performance and the ability to handle class imbalance underscore the potential of our approach for real-world TB detection applications.

Integrating machine learning in electronic health passport based on WHO study and healthcare resources

Healthcare's evolution amid rapid scientific progress demands the efficient management of extensive data. This has given rise to electronic health passports, which play a crucial role in modern healthcare by providing secure access to comprehensive medical histories. This study, exemplified by diabetes, addresses the mounting global prevalence of this disease, which saw cases soar from 108 million in 1980 to 422 million by 2014, resulting in a 5 % rise in diabetes-related premature mortality from 2000 to 2016. Disease prediction based on patient attributes holds potential to optimize healthcare resource allocation. The research aim is to identify new hidden dependencies and parameters in the diagnosis of diabetes, utilizing machine learning techniques. The first stage of this involves a thorough comparison and optimization of various modeling techniques, from Decision Trees (e.g., Random Forest, Adaboost) to advanced Deep Learning models like CNN, RNN, and LSTM networks, aiming to determine the most effective implementation method. Leveraging a subset of the MIMIC-III Critical Care Database, a comprehensive de-identified health-related dataset, this research provides insights into addressing the escalating diabetes challenge. The study revealed that the Gradient Boosting Machine was the most effective for making diagnoses. In the realm of deep learning, the Recurrent Neural Network (RNN) demonstrated the best results. This work aligns with the broader context of utilizing electronic health passports to enhance healthcare efficiency and decision-making in an era characterized by abundant health data. It is essential to emphasize that this work represents the initial phase of a broader research trajectory, and its continuation will be expounded upon in forthcoming publications. Datasethttps://doi.org/10.13026/C2XW26. Dataset licensePhysioNet Credentialed Health Data Use Agreement 1.5.0.

HybridGCN for protein solubility prediction with adaptive weighting of multiple features

The solubility of proteins stands as a pivotal factor in the realm of pharmaceutical research and production. Addressing the imperative to enhance production efficiency and curtail experimental costs, the demand arises for computational models adept at accurately predicting solubility based on provided datasets. Prior investigations have leveraged deep learning models and feature engineering techniques to distill features from raw protein sequences for solubility prediction. However, these methodologies have not thoroughly delved into the interdependencies among features or their respective magnitudes of significance. This study introduces HybridGCN, a pioneering Hybrid Graph Convolutional Network that elevates solubility prediction accuracy through the combination of diverse features, encompassing sophisticated deep-learning features and classical biophysical features. An exploration into the intricate interplay between deep-learning features and biophysical features revealed that specific biophysical attributes, notably evolutionary features, complement features extracted by advanced deep-learning models. Augmenting the model’s capability for feature representation, we employed ESM, a substantial protein language model, to derive a zero-shot learning feature capturing comprehensive and pertinent information concerning protein functions and structures. Furthermore, we proposed a novel feature fusion module termed Adaptive Feature Re-weighting (AFR) to integrate multiple features, thereby enabling the fine-tuning of feature importance. Ablation experiments and comparative analyses attest to the efficacy of the HybridGCN approach, culminating in state-of-the-art performances on the public eSOL and S. cerevisiae datasets.

AN IMPROVED MASK R-CNN: EXTRACTION OF DOOR AND WINDOW INSTANCES ON VILLAGE BUILDING FAÇADE IMAGES

Abstract. Rapid access to the basic structure of village buildings is conducive to the investigation of the load-bearing bodies of village houses and provides data support for disaster assessment and post-disaster rescue and reconstruction. The development of computer vision technology provides new ideas and tools for identifying and extracting basic structures of housing buildings. Considering that the original Mask R-CNN ignores the spatial association and relationship of door and window elements, an advanced deep learning model based on Mask R-CNN network is proposed in this paper to detect and segment the door and window structure from the façade images. The improved network architectures integrate the attention mechanism with the original network, containing an improved Coordinate Attention(CA) module and a relationship module-based head network. The experimental results show that the Average Precision(AP) value of the backbone combined with the improved CA module is increased by 0.7% and 0.7% on regression and segmentation tasks respectively, compared with the original Mask R-CNN network. In the head network based on the relationship module, the calculation strategy of the relational module proposed in this paper increases the AP values of detection and segmentation from 76.7% and 77.7% to 80.6% and 80.0%, respectively.

Anticipating Groundwater Depletion for Water Scarcity Alleviation through Advanced Deep Learning Models

Amidst the challenges posed by climate change, population growth, and pollution, global water availability is diminishing, resulting in critical water shortages. This research proposes a water availability analysis system to tackle the escalating issue exacerbated by urbanization and unsustainable water practices. Conventional methods prove inefficient, prompting the adoption of differentiated analysis through remote sensing and high-resolution mapping for real-time monitoring. The study aims to make a substantial contribution to global water security by providing precise solutions for identifying, managing, and distributing water resources effectively.

AI Pair Programming Tool

Abstract: In the ever-evolving landscape of software development, the need for tools that augment developer productivity is paramount. This paper introduces a novel integration of IntelliJ IDEA, a widely used integrated development environment (IDE), with OpenAI's advanced deep learning models. The synergy between these two technologies aims to elevate the coding experience by providing intelligent code assistance, allowing developers to write code more efficiently and with greater accuracy. Our approach leverages OpenAI's natural language processing capabilities to enhance code suggestions, auto-completions, and error detection within IntelliJ IDEA. By analyzing vast code repositories and learning from diverse coding patterns, our system empowers developers with context-aware recommendations, significantly reducing the cognitive load associated with code creation. This paper presents the architecture of our solution, detailing the seamless integration of OpenAI's models into the IntelliJ IDEA IDE. We discuss the methodology behind training the models, emphasizing their adaptability to various programming languages and project structures. Furthermore, we present empirical results demonstrating the effectiveness of our approach in real-world coding scenarios.

Unlocking the Potential: A Deep Learning Approach for Objective Screening of AML with NPM1 Mutation Via Holotomography

Introduction: As the clinical significance of genetic mutations becomes clearer, diagnostic classifications are continuously evolving, making judicious decisions about implementing costly genetic testing for diagnosis crucial. Intriguing observations suggest that Myelodysplastic Syndrome (MDS) or MDS/Myeloproliferative Neoplasm with NPM1 mutation can rapidly progress to Acute Myeloid Leukemia (AML), leading to ambiguity in acute leukemia diagnosis. Consequently, this finding has led to ambiguity surrounding the diagnostic criteria for acute leukemia with low blast counts. Holotomography (HT) is a cutting-edge microscope that utilizes refractive index (RI) as a contrast medium. It not only provides 2D or 3D images but also offers numerical information about every pixel of cells, promising a more objective diagnosis. Previous studies have successfully distinguished mature cells and blasts using HT. In this study, our aim is to construct a sophisticated deep learning model using data from HT to predict the presence of NPM1 mutations in blasts. This research endeavor holds the potential to enable early detection of these mutations in hematologic malignancies and facilitate the effective utilization of valuable medical resources. Methods: We utilized HT to analyze blasts of AML with mutated NPM1 (experimental group) and AML not otherwise specified (control group) but exhibiting similar morphological characteristics from May 2021 to May 2022. We analyzed a total of 648 blasts from 15 patients in the experimental group and 762 blasts from 24 patients in the control group. Cell segmentation for Rule-based features was performed using ilastik version 1.3.3 and MATLAB 2022a. After the initial segmentation using ilastik's machine learning pixel classification feature, further refinements were made in MATLAB, based on prior knowledge of the size and topology of each structure. Subsequently, volumetric and morphological information was measured within these segments. The cell data of each patient were divided into a training set and a test set. The deep learning algorithm, built using the training set, was then validated with data from the test set. For the deep learning and prediction tasks, we employed a Convolutional Neural Network algorithm (Figure). The classification process involved utilizing both the Rule-based features and the entire feature set, where a linear kernel Support Vector Machine was used. Additionally, we employed the Ensemble average approach, where multiple cells' scores (numerical values associated with the probabilities of each classification) were averaged, and the classification with the highest score was selected as the final prediction. Finally, the deep learning model was validated using previously unused test set data through the Receiver Operating Characteristic (ROC) curve. Results: The dry mass of the nucleolus was significantly increased in AML, NOS; Exp, 0.29341 ± 0.014499 pg; Control, 0.71184 ± 0.033521 pg ( P<0.05). The dry mass of the cytoplasm was higher in AML with mutated NPM1, showing a higher value than that of the nucleolus; Exp, 45.7334 ± 0.50003 pg; Control, 42.9807 ± 0.63882 pg (P<0.05). When validating the deep learning model constructed based on individual cells, the area under the ROC curve (AUC) was found to be 0.8836. Even within the same individual, the cell morphology can be heterogeneous. Therefore, when the data from 5 cells were treated as a single entity for validating the deep learning model, the AUC was 0.9944. Conclusions: It is widely acknowledged that the NPM1 protein, typically localized in the nucleolus, undergoes relocation to the cytoplasm as a result of NPM1 mutation. In this investigation, we devised an advanced deep learning model leveraging numerical data derived from HT, enabling us to prognosticate variations in protein expression based on NPM1 mutations. The HT numerical data exhibited distinctive variations in both nuclear and cytoplasmic analyses contingent on NPM1 mutations. Given the current uncertainty in leukemia diagnosis, partly attributable to the costly selection of genetic testing, we are optimistic that the integration of HT with our sophisticated deep learning model will pave the way for early diagnosis prediction before conducting additional genetic tests.

MetaAc4C: A multi-module deep learning framework for accurate prediction of N4-acetylcytidine sites based on pre-trained bidirectional encoder representation and generative adversarial networks

MotivationN4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in various biological processes. Accurately identifying ac4C sites is of paramount importance for gaining a deeper understanding of their regulatory mechanisms. Nevertheless, the existing experimental techniques for ac4C site identification are characterized by limitations in terms of cost-effectiveness, while the performance of current computational methods in accurately identifying ac4C sites requires further enhancement. ResultsIn this paper, we present MetaAc4C, an advanced deep learning model that leverages pre-trained bidirectional encoder representations from transformers (BERT). The model is based on a bi-directional long short-term memory network (BLSTM) architecture, incorporating attention mechanism and residual connection. To address the issue of data imbalance, we adapt generative adversarial networks to generate synthetic feature samples. On the independent test set, MetaAc4C surpasses the current state-of-the-art ac4C prediction model, exhibiting improvements in terms of ACC, MCC, and AUROC by 2.36%, 4.76%, and 3.11%, respectively, on the unbalanced dataset. When evaluated on the balanced dataset, MetaAc4C achieves improvements in ACC, MCC, and AUROC by 2.6%, 5.11%, and 1.01%, respectively. Notably, our approach of utilizing WGAN-GP augmented training RNA samples demonstrates even superior performance compared to the SMOTE oversampling method.

Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further.

Enhancing fetal electrocardiogram classification: A hybrid approach incorporating multimodal data fusion and advanced deep learning models

Enhancing fetal electrocardiogram classification: A hybrid approach incorporating multimodal data fusion and advanced deep learning models

CSformer: Enhancing deep learning efficiency for intelligent IoT

The rapid development of deep learning technology has led to increasing demand for more intelligent, automated, and humanized Internet of Things (IoT) devices. Deep learning models, while endowing IoT devices with the capability to learn higher-level features, concurrently impose more demanding computational and storage prerequisites on the hardware. To tackle the challenge and enable the practical application of deep learning models in IoT devices, we propose a novel efficient Transformer called CSformer, which incorporates intra-layer cluster and inter-layer selection. Intra-layer cluster is performed using a k-means++ based generation algorithm to improve cluster accuracy. To address the issues of information loss caused by clustering, we propose cluster center information enhancement and clustering loss calculation modules. The inter-layer selection strategy selects tokens according to their contribution, consistently diminishes redundancy, and prioritizes the retention of crucial information. By consistently reducing the sequence length, the inter-layer selection significantly improves training speed and reduces the memory occupation of the model. The experimental results indicate that in two common scenarios for intelligent IoT, namely text classification and sequence labeling, CSformer significantly outperforms the baseline models. Specifically, in the text classification task, our model achieves an average 22.66% reduction in memory consumption, a 37.98% decrease in time consumption, and a superior 9.58% performance improvement compared to baseline models across six datasets. Additional experiments substantiate the efficacy of the intra-layer cluster and inter-layer selection modules, as demonstrated through ablation experiments, overall performance, and visualization. The intra-layer cluster module enhances the performance of existing models by achieving more precise clustering and mitigating information loss, leading to significant performance improvements. The inter-layer selection module enhances the efficiency of existing studies by reducing model memory consumption and improving computational efficiency through the selective retention of essential tokens. This can effectively facilitate future research in applying advanced deep learning models to intelligent IoT, expanding the range of application scenarios and tasks within the field of intelligent IoT.

Unraveling COVID-19 Progression: A Comprehensive Com-parison of Advanced Deep Learning Methods for Precise Pre-dictions

The COVID-19 pandemic has significantly impacted the United Arab Emirates (UAE), necessitating effective and accurate forecasting tools to inform public health policies and strategies. This study presents a comparative analysis of advanced deep-learning models for predicting COVID-19 cases in the UAE. We investigate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer perceptron, and Recurrent Neural Networks (RNN). The models are trained and evaluated using a comprehensive dataset of confirmed cases, demographic information, and relevant socio-economic indicators. The models are further optimized using a Bayesian optimizer and comparison is performed before and after the optimization of models. We have used predictive and perspective analytics on the COVID-19 dataset. Our research goal is to identify the most accurate and reliable model for forecasting COVID-19 cases in the region.
 The results demonstrate the effectiveness of these deep learning techniques in predicting COVID-19 cases, with each model exhibiting varying levels of accuracy and precision. A thorough and rigorous evaluation of the models' performances reveals the most suitable architecture for the UAE's specific context. This study contributes to the ongoing efforts to combat the pandemic by providing valuable insights into the application of advanced deep-learning models for accurate and timely COVID-19 case predictions. It was found that the RNN model performed the best without any optimization. The findings have significant implications for public health decision-making, enabling authorities to develop targeted and data-driven interventions to curb the spread of the virus and mitigate its impact on the UAE's population. This demonstrates the potential of deep learning algorithms in handling complex datasets and making accurate predictions which is a valuable capability to enhance accuracy in professional and healthcare environments.

Multiclass weed identification using semantic segmentation: An automated approach for precision agriculture

Accurate identification and categorization of numerous weed species are critical for implementing effective control measures and management methods in precision agriculture. Manual weed treatment is time-consuming, labor-intensive, and poses risks of human pesticide exposure. Therefore, the development of automated weed management systems is highly desirable. This study aims to propose an automated approach for multiclass weed identification using semantic segmentation, with the goal of improving weed control techniques, reducing pesticide usage, and enhancing crop yields in a sustainable manner. To address the research objective, we created a novel multiclass weed dataset, focusing on two weed categories found in a brinjal farm located in Gorakhpur, Uttar Pradesh, India during the 2022 field seasons. The dataset covers various developmental phases and was captured under ambient lighting conditions. Leveraging transfer learning, we evaluated four advanced deep learning models to establish a benchmark for weed identification. Among the evaluated models, the U-Net-based Inception-ReseNetV2 achieved the highest F1-score of 96.78%, while the other three models attained F1-scores above 91.0%. These findings demonstrate the efficacy of the proposed approach in accurately identifying and categorizing weeds in agricultural fields. The results of this research provide a foundation for further investigations on weed detection and localization in field environments. The use of semantic segmentation for multiclass weed identification can significantly enhance the efficiency and effectiveness of weed management operations, resulting in reduced pesticide usage and improved crop yields. By adopting automated weed management systems, farmers can minimize labor requirements, save time, and mitigate the risks associated with human pesticide exposure.

Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models

BackgroundManual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset.ResultsThe best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives.ConclusionsAlthough correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts—limiting the need for model retraining.

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

MelodyAI: AI-Powered Melodies Generation

MelodyAI AI-Powered Melodies Generation embodies a cutting-edge exploration at the intersection of artificial intelligence and artistic expression. Drawing inspiration from a seminal research paper on music generation with neural networks, we set out to develop an innovative platform for crafting original musical compositions. Harnessing the potential of advanced deep learning models—such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and attention mechanisms—we aspire to revolutionize the way music is created and experienced. Our approach involves the intricate study of the original research, enabling us to adapt and extend the findings to construct a powerful AI-driven system. Our project focuses on unraveling the complexity of musical language and structure, aiming to generate melodies that resonate with human-like creativity and emotion. By leveraging a diverse and extensive dataset and implementing state-of-the-art machine learning techniques, we endeavor to compose melodies that captivate and inspire. MelodyAI AI-Powered Melodies Generation stands as a testament to the transformative potential of machine learning in the realm of artistic innovation. Through this project, we not only aim to craft melodies that touch the soul but also to stimulate a new era of creative collaboration between humans and machines. Keywords: MelodyAI, Music Generation, Artificial Intelligence, Deep Learning Models, Recurrent Neural Networks, Long Short-Term Memory (LSTM), Attention Mechanism.

Joint DR-DME grading classification using optimal feature selection-based deep graph correlation network

Diabetic macular edema (DME) and diabetic retinopathy (DR) are the leading causes of human blindness, and accurate grading of individual DME, individual DR, and the joint DR-DME is very important for the diagnosis of human eye diseases. However, the conventional methods failed to separate the features such as disease-specific, disease-dependent, and joint DR-DME, which resulted in poor grading accuracy. In addition, optimal feature selection is also vital in DR-DME grading classification for improving the performance of joint DR-DME grading classification. Therefore, this work focuses on the implementation of an advanced deep graph correlation learning model based on a joint DR-DME network (JDD-Net) for disease detection and classification from color fundus images. Initially, convolutional block attention module (CBAM) and joint disease attention (JDA) modules are combined to extract the DR-specific, DME-specific, and joint DR-DME disease-dependent features. Here, interdependent DR and DME features are separated by a CBAM-based channel-spatial split attention mechanism. In addition, an iterative random forest network (IRF-Net) is used to select the optimal features by adopting fast machine learning properties. Finally, a deep graph correlation network (DGCN) is used to classify the different diseases using a pre-trained model. The simulations conducted on the Indian Diabetic Retinopathy Image Dataset (IDRiD) disclose that the proposed JDD-Net results in improved individual DR, individual DME, and joint DR-DME performance as compared to state-of-the-art approaches with DR, DME, and joint DR-DME accuracy of 99.53%, 99.1%, and 99.01%, respectively.

Effective Brain Stroke Prediction with Deep Learning Model by Incorporating YOLO_5 and SSD

Ischemic stroke is a life-threatening disorder that significantly reduces a person’s lifespan. The timely diagnosis of stroke heavily relies on medical imaging techniques such as magnetic resonance imaging (MRI), computerized tomography (CT), and x-ray imaging. However, the manual localization and analysis of these images can be time-consuming and yield less accurate results. To address this challenge, we propose the implementation of deep-learning object detection techniques for computerized lesion identification in medical images. In this study, we employ three categories of deep learning object identification networks: deep convolutional neural network (DCNN), you only look once (YOLO) 5, and single-shot detector (SSD). By leveraging these advanced deep learning models, we aim to reduce the effort and time required for screening and analyzing a significant number of daily medical images, including MRI, CT, and x-ray images. With the addition of YOLO5 and SSD among these networks, the accuracy achieved was 96.43%, demonstrating their effectiveness in accurately identifying lesions associated with ischemic stroke.

GLCM-Based FBLS: A Novel Broad Learning System for Knee Osteopenia and Osteoprosis Screening in Athletes

Due to the physical strain experienced during intense workouts, athletes are at a heightened risk of developing osteopenia and osteoporosis. These conditions not only impact their overall health but also their athletic performance. The current clinical screening methods for osteoporosis are limited by their high radiation dose, complex post-processing requirements, and the significant time and resources needed for implementation. This makes it challenging to incorporate them into athletes’ daily training routines. Consequently, our objective was to develop an innovative automated screening approach for detecting osteopenia and osteoporosis using X-ray image data. Although several automated screening methods based on deep learning have achieved notable results, they often suffer from overfitting and inadequate datasets. To address these limitations, we proposed a novel model called the GLCM-based fuzzy broad learning system (GLCM-based FBLS). Initially, texture features of X-ray images were extracted using the gray-level co-occurrence matrix (GLCM). Subsequently, these features were combined with the fuzzy broad learning system to extract crucial information and enhance the accuracy of predicting osteoporotic conditions. Finally, we applied the proposed method to the field of osteopenia and osteoporosis screening. By comparing this model with three advanced deep learning models, we have verified the effectiveness of GLCM-based FBLS in the automatic screening of osteoporosis for athletes.