Automatic Classification Model Research Articles

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost.

Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources. We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token. The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA). Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

A Pruning Method Combined with Resilient Training to Improve the Adversarial Robustness of Automatic Modulation Classification Models

A Pruning Method Combined with Resilient Training to Improve the Adversarial Robustness of Automatic Modulation Classification Models

Automatic classification of spinal osteosarcoma and giant cell tumor of bone using optimized DenseNet

ObjectiveThis study aims to explore an optimized deep-learning model for automatically classifying spinal osteosarcoma and giant cell tumors. In particular, it aims to provide a reliable method for distinguishing between these challenging diagnoses in medical imaging. MethodsThis research employs an optimized DenseNet model with a self-attention mechanism to enhance feature extraction capabilities and reduce misclassification in differentiating spinal osteosarcoma and giant cell tumors. The model utilizes multi-scale feature map extraction for improved classification accuracy. The paper delves into the practical use of Gradient-weighted Class Activation Mapping (Grad-CAM) for enhancing medical image classification, specifically focusing on its application in diagnosing spinal osteosarcoma and giant cell tumors. The results demonstrate that the implementation of Grad-CAM visualization techniques has improved the performance of the deep learning model, resulting in an overall accuracy of 85.61%. Visualizations of images for these medical conditions using Grad-CAM, with corresponding class activation maps that indicate the tumor regions where the model focuses during predictions. ResultsThe model achieves an overall accuracy of 80% or higher, with sensitivity exceeding 80% and specificity surpassing 80%. The average area under the curve AUC for spinal osteosarcoma and giant cell tumors is 0.814 and 0.882, respectively. The model significantly supports orthopedics physicians in developing treatment and care plans. ConclusionThe DenseNet-based automatic classification model accurately distinguishes spinal osteosarcoma from giant cell tumors. This study contributes to medical image analysis, providing a valuable tool for clinicians in accurate diagnostic classification. Future efforts will focus on expanding the dataset and refining the algorithm to enhance the model's applicability in diverse clinical settings.

An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds.

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a common sleep-related breathing disorder that significantly impacts the daily lives of patients. Currently, the diagnosis of OSAHS relies on various physiological signal monitoring devices, requiring a comprehensive Polysomnography (PSG). However, this invasive diagnostic method faces challenges such as data fluctuation and high costs. To address these challenges, we propose a novel data-driven Audio-Semantic Multi-Modal model for OSAHS severity classification (i.e., ASMM-OSA) based on patient snoring sound characteristics. In light of the correlation between the acoustic attributes of a patient's snoring patterns and their episodes of breathing disorders, we utilize the patient's sleep audio recordings as an initial screening modality. We analyze the audio features of snoring sounds during the night for subjects suspected of having OSAHS. Audio features were augmented via PubMedBERT to enrich their diversity and detail and subsequently classified for OSAHS severity using XGBoost based on the number of sleep apnea events. Experimental results using the OSAHS dataset from a collaborative university hospital demonstrate that our ASMM-OSA audio-semantic multimodal model achieves a diagnostic level in automatically identifying sleep apnea events and classifying the four-class severity (normal, mild, moderate, and severe) of OSAHS. Our proposed model promises new perspectives for non-invasive OSAHS diagnosis, potentially reducing costs and enhancing patient quality of life.

Data-driven automatic classification model for construction accident cases using natural language processing with hyperparameter tuning

The construction industry, while vital to societal progress, is marred by a high incidence of accidents and injuries. Manual classification of accident cases is intensive and susceptible to human bias. This study addresses this challenge by developing an automated accident case classification system for the construction industry using Natural Language Processing and machine learning techniques. This study was conducted using the following steps: (1) Establishment of dataset (2) Korean Natural Language Processing (3) Selection of machine learning models (4) Model evaluation. The models exhibited competitive performance, demonstrating high accuracy, precision, and recall rates across all classification tasks. XGBoost outperformed NB, SVM, and KNN for accident type, facility type, and work type with accuracy of 0.80, 0.56, and 0.67, respectively. The results also provided insights into the factors influencing accident classification. This study contributes to construction safety by providing a data-driven foundation for safety decision-making, resource allocation, and benchmarking.

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.

Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.

Simplified Convolutional Neural Network Model for Automatic Classification of Retinal Diseases from Optical Coherence Tomography Images

Optical coherence tomography (OCT) allows for direct and immediate imaging of the morphology of retinal tissue. It has become a crucial imaging modality for diagnosing eye problems in ophthalmology. One of the most significant morphological characteristics of the retina is the structure of the retinal layers, which provides important evidence for diagnostic purposes and is related to a variety of retinal diseases. In this paper, a convolutional neural network (CNN) model is proposed that can identify the difference between a normal retina and three common macular diseases: Diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV). This proposed model was trained and tested on an open source dataset of OCT images also with professional disease classifications such as DME, CNV, Drusen, and Normal. The suggested model has achieved 98.3% overall classification accuracy, with only 7 wrong classifications out of 368 test samples. The suggested model significantly outperforms other models that made use of the identical dataset. The final results show that the suggested model is particularly adapted to the detection of retinal disorders in ophthalmology centers.

Automatic Gait Classification Model Empowered by Machine Learning for People with and without Osteoporosis Using Smart Walker

Osteoporosis constitutes a significant public health concern necessitating proactive prevention, treatment, and monitoring efforts. Timely identification holds paramount importance in averting fractures and alleviating the overall disease burden. The realm of osteoporosis diagnosis has witnessed a surge in interest in machine learning applications. This burgeoning technology excels at recognizing patterns and forecasting the onset of osteoporosis, paving the way for more efficacious preventive and therapeutic interventions. Smart walkers emerge as valuable tools in this context, serving as data acquisition platforms for datasets tailored to machine learning techniques. These datasets, trained to discern patterns indicative of osteoporosis, play a pivotal role in enhancing diagnostic accuracy. In this study, encompassing 40 participants—20 exhibiting robust health and 20 diagnosed with osteoporosis—data from force sensors embedded in the handlebars of conventional walkers were gathered. A windowing action was used to increase the size of the dataset. The data were normalized, and k-fold cross-validation was applied to assess how well our model performs on untrained data. We used multiple machine learning algorithms to create an accurate model for automatic monitoring of users’ gait, with the Random Forest classifier performing the best with 95.40% accuracy. To achieve the best classification accuracy on the validation dataset, the hyperparameters of the Random Forest classifier were further adjusted on the training data. The results suggest that machine learning-based automatic monitoring of gait parameters could lead to accurate, non-laborious, cost-effective, and efficient diagnostic tools for osteoporosis and other musculoskeletal disorders. Further research is needed to validate these findings.

Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network

AbstractAn ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract

Utilizing EfficientNet for sheep breed identification in low-resolution images

Automatically recognizing sheep breeds is highly valuable for the sheep farming industry, allowing farmers to pinpoint their specific business needs. Accurately distinguishing between sheep breeds poses a challenge for numerous farmers with limited expertise. Although biometric-based identification offers a feasible solution, its application becomes impractical when assessing large numbers of sheep in real-time. Therefore, the implementation of an automatic sheep classification model that can replicate the breed identification skills of a sheep breed expert can come in handy. This would be particularly beneficial for novice farmers who could utilize handheld devices for breed classification. To address this objective, we propose employing a convolutional neural network (CNN) model capable of rapidly and accurately identifying sheep breeds from low-resolution images. Our experiment utilized a dataset of 1680 facial images representing four distinct sheep breeds. We conducted experiments on the dataset using various EfficientNet models and found that EfficientNetB5 achieved the highest performance with 97.62 % accuracy on a 10 % test split. The classification model we developed has the potential to assist sheep farmers in efficiently distinguishing between different breeds, facilitating more precise assessments and sector-specific classification for various businesses within the industry.

Application of Deep Learning to Diagnose and Classify Adolescent Idiopathic Scoliosis

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r2≥0.9552, P<0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency.

Convolutional neural network model for the qualitative evaluation of geometric shape of carrot root

The main objective of the study is the development of an automatic carrot root classification model, marked as CR-NET, with the use of a Convolutional Neural Network (CNN). CNN with a constant architecture was built, consistingof an alternating arrangement of five Conv2D, MaxPooling2D and Dropout classes, for which in the Python 3.9 programming language a calculation algorithm was developed. It was found that the classification process of the carrot root images was carried out with an accuracy of 89.06%, meaning that 50 images were misclassified. The highest number of 21 erroneously classified photographs were from the extra class, of which 15 to the first class, thus not resulting in significant loss. However, assuming the number of refuse as the classification basis, the model accuracy greatly increases to 98.69%, as only 6 photographs were erroneously assigned.

An ensemble deep learning model for automatic classification of cotton leaves diseases

&lt;p&gt;Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models.&lt;/p&gt;

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

EDLNet: ensemble deep learning network model for automatic brain tumor classification and segmentation

The brain’s abnormal and uncontrollable cell partitioning is a severe cancer disease. The tissues around the brain or the skull induce this tumor to develop spontaneously. For the treatment of a brain tumor, surgical techniques are typically preferred. Deep learning models in the biomedical field have recently attracted a lot of attention for detecting and treating diseases. This article proposes a new Ensemble Deep Learning Network (EDLNet) model. This research uses the Modified Faster RCNN approach to classify brain MRI scan images into cancerous and non-cancerous. A deep recurrent convolutional neural network (DRCNN)-based diagnostic method for early-stage brain tumor segmentation is presented. The evaluation outcomes show that the proposed tumor classification and segmentation model’s performance accurately segments tissues from MRI images. For the analysis of the proposed model, two different publicly available datasets (D1&D2) are used. For D1 and D2 datasets, a total of 99.76% and 99.87% accuracies are achieved by the proposed model. The performance results of the proposed model are more effective than the state-of-the-art network models as per the experimental results. Communicated by Ramaswamy H. Sarma

Ensemble Learning Approach for Digital Communication Modulation’s Classification

This work uses artificial intelligence to create an automatic solution for the modulation's classification of various radio signals. This project is a component of a lengthy communications intelligence process that aims to find an automated method for demodulating, decoding, and deciphering communication signals. As a result, the work we did involved selecting the database required for supervised deep learning, assessing the performance of current methods on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy. In order to use the current automatic classification models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for communication signals that is simple to work with and implement. With an accuracy of up to 95%, this solution's optimum and sturdy architecture decides the type of modulation on its own.

Convolutional neural network model for automatic recognition and classification of pancreatic cancer cell based on analysis of lipid droplet on unlabeled sample by 3D optical diffraction tomography

IntroductionPancreatic cancer cells generally accumulate large numbers of lipid droplets (LDs), which regulate lipid storage. To promote rapid diagnosis, an automatic pancreatic cancer cell recognition system based on a deep convolutional neural network was proposed in this study using quantitative images of LDs from stain-free cytologic samples by optical diffraction tomography. MethodsWe retrieved 3D refractive index tomograms and reconstructed 37 optical images of one cell. From the four cell lines, the obtained fields were separated into training and test datasets with 10,397 and 3,478 images, respectively. Furthermore, we adopted several machine learning techniques based on a single image-based prediction model to improve the performance of the computer-aided diagnostic system. ResultsPancreatic cancer cells had a significantly lower total cell volume and dry mass than did normal pancreatic cells and were accompanied by greater numbers of lipid droplets (LDs). When evaluating multitask learning techniques utilizing the EfficientNet-b3 model through confusion matrices, the overall 2-category accuracy for cancer classification reached 96.7 %. Simultaneously, the overall 4-category accuracy for individual cell line classification achieved a high accuracy of 96.2 %. Furthermore, when we added the core techniques one by one, the overall performance of the proposed technique significantly improved, reaching an area under the curve (AUC) of 0.997 and an accuracy of 97.06 %. Finally, the AUC reached 0.998 through the ablation study with the score fusion technique. DiscussionOur novel training strategy has significant potential for automating and promoting rapid recognition of pancreatic cancer cells. In the near future, deep learning-embedded medical devices will substitute laborious manual cytopathologic examinations for sustainable economic potential.

Automated Diagnosis of Bone Metastasis by Classifying Bone Scintigrams Using a Self-defined Deep Learning Model.

Patients with cancer can develop bone metastasis when a solid tumor invades the bone, which is the third most commonly affected site by metastatic cancer, after the lung and liver. The early detection of bone metastases is crucial for making appropriate treatment decisions and increasing survival rates. Deep learning, a mainstream branch of machine learning, has rapidly become an effective approach to analyzing medical images. To automatically diagnose bone metastasis with bone scintigraphy, in this work, we proposed to cast the bone metastasis diagnosis problem into automated image classification by developing a deep learning-based automated classification model. A self-defined convolutional neural network consisting of a feature extraction sub-network and feature classification sub-network was proposed to automatically detect lung cancer bone metastasis, with a feature extraction sub-network extracting hierarchal features from SPECT bone scintigrams and feature classification sub-network classifying high-level features into two categories (i.e., images with metastasis and without metastasis). Using clinical data of SPECT bone scintigrams, the proposed model was evaluated to examine its detection accuracy. The best performance was achieved if the two images (i.e., anterior and posterior scans) acquired from each patient were fused using pixel-wise addition operation on the bladder-excluded images, obtaining the best scores of 0.8038, 0.8051, 0.8039, 0.8039, 0.8036, and 0.8489 for accuracy, precision, recall, specificity, F-1 score, and AUC value, respectively. The proposed two-class classification network can predict whether an image contains lung cancer bone metastasis with the best performance as compared to existing classical deep learning models. The high accumulation of 99mTc MDP in the urinary bladder has a negative impact on automated diagnosis of bone metastasis. It is recommended to remove the urinary bladder before automated analysis.

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.).

The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.