Biomedical Engineering: Applications, Basis and Communications最新文献_第2页

GRASPING FORCE ESTIMATION FROM TWO-CHANNEL ELECTROMYOGRAPHY SIGNALS USING EXTREME LEARNING MACHINE 基于极限学习机的双通道肌电信号抓取力估计

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-11-10 DOI: 10.4015/s1016237223500345

Supornpit Na Pibul, Pornchai Phukpattaranont

{"title":"GRASPING FORCE ESTIMATION FROM TWO-CHANNEL ELECTROMYOGRAPHY SIGNALS USING EXTREME LEARNING MACHINE","authors":"Supornpit Na Pibul, Pornchai Phukpattaranont","doi":"10.4015/s1016237223500345","DOIUrl":"https://doi.org/10.4015/s1016237223500345","url":null,"abstract":"This research presents a method to select the best parameters of an extreme learning machine (ELM) for estimating grasping force from two-channel surface electromyography (sEMG) signals. Its advantages compared to the use of multi-channel sEMG signals include faster computing, lower electrode costs, and easier usage. The proposed method is appropriate for certain applications where time is very important but accuracy can be slightly compromised. The study recorded sEMG signals from 20 healthy volunteers, 10 males and 10 females, aged 22-52 years. The recorded sEMG signals were used in testing the proposed optimization method for grasping force estimation. It was found that the proposed method for optimizing the number of nodes, gain, and factor values would make the force estimation more efficient. The results show that the optimum number of nodes, gain, and factor values is 4, 0.05, and 1.0045, respectively. The resulting root mean square error and correlation coefficient values in force estimation from the ELM optimization method were 2.5642 and 0.9287, respectively, when the computing time was only 0.0038 s. These results show the feasibility of the proposed method for estimating force using only two-channel sEMG signals. As a result, real-time implementation, such as force estimation in robotic hand control for rehabilitation using sEMG signals, can be achieved efficiently.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ULTRASOUND-BASED MACHINE LEARNING-AIDED DETECTION OF UTERINE FIBROIDS: INTEGRATING VISION TRANSFORMER FOR IMPROVED ANALYSIS 基于超声的机器学习辅助子宫肌瘤检测:集成视觉变压器以改进分析

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-11-07 DOI: 10.4015/s1016237223500333

U. P. Prinith Kaveramma, U. Snekhalatha, Varshini Karthik, M. Anuradha

{"title":"ULTRASOUND-BASED MACHINE LEARNING-AIDED DETECTION OF UTERINE FIBROIDS: INTEGRATING VISION TRANSFORMER FOR IMPROVED ANALYSIS","authors":"U. P. Prinith Kaveramma, U. Snekhalatha, Varshini Karthik, M. Anuradha","doi":"10.4015/s1016237223500333","DOIUrl":"https://doi.org/10.4015/s1016237223500333","url":null,"abstract":"The primary objective of this study is to segment the uterine fibroids (leiomyoma) from the ultrasound images of the uterus through semantic segmentation, followed by second-order statistical feature extraction using the Gray-level Co-occurrence Matrix (GLCM). The next objective of the study is to compare the performance of the state-of-the-art method namely Vision Transformer (ViT) with three different machine learning (ML) classifiers such as the Support Vector Machine (SVM), Logistic Regression (LR) and [Formula: see text]-Nearest Neighbor ([Formula: see text]-NN) to classify the images into uterine fibroid and normal. The dataset consists of 50 ultrasound images of uterine fibroids and 50 normal images. Then the images are segmented using region-growing-based semantic segmentation followed by feature extraction and classification using the ML and deep learning (DL) classifiers. Among the ML classifiers, SVM produced a good accuracy of 93.1% compared to the other classifiers. ViT produced an excellent classification accuracy of 97.5%. Hence, ViT outperformed compared to the ML classifiers in uterine fibroid detection. These findings have important implications for clinical practice, as they could help physicians to diagnose and treat uterine fibroids more effectively.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"52 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANALYSIS OF ELECTROHYSTEROGRAM SIGNALS AND PREDICTION OF PRETERM BIRTHS USING MACHINE LEARNING 利用机器学习分析子宫电图信号和预测早产

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-11-04 DOI: 10.4015/s1016237223500291

Dharini Raghavan, H. H. Adithya, S. Raghuram, K. V. Suma, Tricha Kulhalli

{"title":"ANALYSIS OF ELECTROHYSTEROGRAM SIGNALS AND PREDICTION OF PRETERM BIRTHS USING MACHINE LEARNING","authors":"Dharini Raghavan, H. H. Adithya, S. Raghuram, K. V. Suma, Tricha Kulhalli","doi":"10.4015/s1016237223500291","DOIUrl":"https://doi.org/10.4015/s1016237223500291","url":null,"abstract":"The World Health Organization (WHO) estimates that over 15 million infants are born before the entire period of pregnancy. Over a million neonatal deaths occurred in 2015 as a result of preterm delivery, which is the prime cause for most deaths among children under the age of five. Although preterm birth has no hereditary link, only 10% of preterm deliveries in high-income settings result in deaths, compared to a mortality rate of up to 90% in low-income nations. When preterm cases are discovered, in addition to enhanced care provided at home for the expecting mother, the growing foetus may also benefit from medication, hospitalization for the duration of the pregnancy, or both. Low-income countries also struggle with a lack of access to a comprehensive healthcare system, which makes it difficult to proactively detect premature births. Machine learning techniques have a great potential to improve this situation by providing an intelligent framework for the detection of critical situations and consequently alerting the individual in cases of anomalies. This will require further follow-up with medical specialists. In this work, we investigate the application of deep learning and machine learning techniques for the analysis of electrohysterogram (EHG) data, which are uterine electrical impulses used to detect preterm deliveries. The TPEHG dataset is used to train a variety of machine learning classifiers, such as Support Vector Machines, Logistic Regression, and Decision Trees, as well as Deep Neural Networks like Convolutional Neural Networks and LSTMs. Additionally, we use plurality voting to build an ensemble of various neural networks and binary classifiers that are trained to classify EHG signals. The ensemble machine learning classifier with five base classifiers produced the best results overall, with an accuracy of 98.99%, sensitivity of 98.3%, and specificity of 97.9% outperforming several state-of-the-art algorithms for preterm birth detection.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"55 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BREAST CANCER DETECTION AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES BASED ON OPTIMIZATION-ENABLED DEEP LEARNING 基于优化深度学习的组织病理学图像的乳腺癌检测和分类

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-11-03 DOI: 10.4015/s101623722350028x

Samla Salim, R. Sarath

{"title":"BREAST CANCER DETECTION AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES BASED ON OPTIMIZATION-ENABLED DEEP LEARNING","authors":"Samla Salim, R. Sarath","doi":"10.4015/s101623722350028x","DOIUrl":"https://doi.org/10.4015/s101623722350028x","url":null,"abstract":"Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Among all types of cancers, Breast Cancer (BC) is a substantial research subject in the medical imaging area, because it is a serious disease and primary reason for death in women. Proper diagnosis helps patients to get adequate treatment, enhancing the probability of surviving. Because of the poor contrast and unclear structure of tumor cells in the images, automatic segmenting of breast tumors remains a difficult task. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. To address these limitations, an efficient mechanism for BC detection and classification using histopathological images is proposed, which employs a DenseNet-based Chronological Circle Inspired Optimization Algorithm (CCIOA). Deep Learning (DL) approaches are used in the suggested BC classification scheme to precisely segment and identify the BC. The segmentation is done using ResuNet++, and an efficient optimization method called Invasive Water Ebola Optimization (IWEO) is used to fine-tune the DL network’s parameters. Furthermore, DenseNet is utilized for BC detection, while CCIOA is used for DenseNet training. The CCIOA-DenseNet is evaluated using the metrics of accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). Experiment results show that the CCIOA-DenseNet attained better accuracy of 0.971, TPR of 0.966, and TNR of 0.954.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"28 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135873589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AUTOMATIC ELECTROENCEPHALOGRAPHIC SOURCE SEPARATION STRATEGIES FOR SEIZURE PREDICTION APPLICATION 自动脑电图源分离策略在癫痫发作预测中的应用

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-11-02 DOI: 10.4015/s1016237223500321

Banu Priya Prathaban, Subash Rajendran, Ramachandran Balasubramanian

{"title":"AUTOMATIC ELECTROENCEPHALOGRAPHIC SOURCE SEPARATION STRATEGIES FOR SEIZURE PREDICTION APPLICATION","authors":"Banu Priya Prathaban, Subash Rajendran, Ramachandran Balasubramanian","doi":"10.4015/s1016237223500321","DOIUrl":"https://doi.org/10.4015/s1016237223500321","url":null,"abstract":"Electroencephalography (EEG) is a common clinical method of recording the electrical activity of the brain. EEG can record High-Frequency Oscillations ([Formula: see text]80 HZ), which carry appropriate information regarding Epilepsy. High-Frequency Oscillations (HFO) serve as a potential biomarker for Epileptogenesis. EEG signals are often prone to artifact corruptions, which mislead the clinicians by the incorrect signal interpretations. Therefore, automatic artifact removal approach is a key phase in all the Brain-Computer Interface (BCI) applications. In this work, the automatic artifact identification and removal strategy without consuming any supplementary reference channel using two different approaches is developed and discussed. A proficient novel Modified Online Bi-Conjugate Gradient-based Independent Component Analysis (MOBICA) is developed. An efficient threshold-based peak detection and removal strategy, Sparsity-based Artifact Removal Technique (SART) is constructed, where Principle Component Analysis (PCA) is replaced with Singular Value Decomposition (SVD) of the K-SVD algorithm. Both the proposed models are evaluated on two different datasets like CHB-MIT and SRM scalp data recordings. Both the MOBICA and SART algorithms removed the artifactual component parting the intact EEG source component. Finally, the performance of the proposed agenda is compared with the conventional approaches. Our MOBICA and SART algorithms remove the artifactual component parting the intact EEG source component. Empirical results of SART on CHB-MIT and SRM databases of 52 EEG recordings outperform MOBICA maintaining least Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and high Signal to Artifact Ratio (SAR), Mutual Information (MI), and Correlation Coefficient (CC). The proposed strategy vows to be a promising solution for artifact removal in the clinical use of EEG signals and in BCI applications.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"33 S1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DETECTION OF CHRONIC VENOUS INSUFFICIENCY CONDITION USING TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS BASED ON THERMAL IMAGES 基于热图像的卷积神经网络迁移学习检测慢性静脉功能不全

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-10-20 DOI: 10.4015/s1016237223500308

Nithyakalyani Krishnan, P. Muthu

{"title":"DETECTION OF CHRONIC VENOUS INSUFFICIENCY CONDITION USING TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS BASED ON THERMAL IMAGES","authors":"Nithyakalyani Krishnan, P. Muthu","doi":"10.4015/s1016237223500308","DOIUrl":"https://doi.org/10.4015/s1016237223500308","url":null,"abstract":"Chronic Venous Insufficiency (CVI) is a venous incompetence condition that leads to improper blood circulation from the lower limbs towards the heart. This occurs as a result of blood pooling in the veins of the leg, resulting in twisted, dilated, and tortuous veins. Aging, obesity, prolonged standing or sitting, and lack of mobility are all important causes of the occurrence of this chronic disease. The cost of CVI diagnosis and treatment is extremely high. Infrared thermographic image analysis is used for early detection and reduces the cost of diagnosis. Deep learning (DL) techniques play an important role in early prediction and may aid clinicians in diagnosing CVI. An automated classification model will assist the physician in making a precise diagnosis of the abnormal vein and treating the patient according to the severity of the condition. There is a need for a model that can perform successful classification without the need for pre-processing when compared to the traditional machine learning (ML) methods that depend on ideal manual feature extraction to achieve optimal outcomes. In this research, we recommend the customized DenseNet-121 architecture for CVI detection and compare it with other advanced DL models to determine its efficacy. DenseNet-121 and other pre-trained convolutional neural network models, including EfficientNetB0 and Inception_v3, were trained using a transfer learning strategy. The experimental findings indicate that the proposed modified DenseNet-121 model outperformed other classical methods. The reported results provide evidence of the robustness of the suggested method in addition to the high accuracy that it possessed, as shown by the overall testing accuracy of 97.4%. Thus, this study can be considered as a non-invasive and cost-effective approach for diagnosing chronic venous insufficiency condition in lower extremity.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EVALUATION OF THE EFFECT OF POLY (𝜀-CAPROLACTONE)/POLY (L-LACTIC) ACID/GELATIN NANOFIBER 3D SCAFFOLD CONTAINING RESVERATROL ON BONE REGENERATION 含白藜芦醇的聚(𝜀-caprolactone)/聚(l -乳酸)酸/明胶纳米纤维三维支架骨再生效果评价

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-10-01 DOI: 10.4015/s1016237223500278

Hossein Kargar Jahromi, Morteza Alizadeh, Arian Ehterami, Ahmad Vaez, Danial Cheraghali, Leila Chegini, Nariman Rezaei Kolarijani, Majid Salehi

{"title":"EVALUATION OF THE EFFECT OF POLY (𝜀-CAPROLACTONE)/POLY (L-LACTIC) ACID/GELATIN NANOFIBER 3D SCAFFOLD CONTAINING RESVERATROL ON BONE REGENERATION","authors":"Hossein Kargar Jahromi, Morteza Alizadeh, Arian Ehterami, Ahmad Vaez, Danial Cheraghali, Leila Chegini, Nariman Rezaei Kolarijani, Majid Salehi","doi":"10.4015/s1016237223500278","DOIUrl":"https://doi.org/10.4015/s1016237223500278","url":null,"abstract":"Bone defects affect many people and impose expenses of costly treatment with possible complications. This study aims to investigate a novel Poly ([Formula: see text]-caprolactone)/Poly (L-lactic) acid/Gelatin nanofiber [PCL/PLA/GNF] scaffold containing 5% resveratrol (Resv) which was manufactured via thermally induced phase separation technique (TIPS), and its applicability for bone defect treatment. Gelatin nanofiber (GNF) was synthesized via the electrospinning method and mixed with PCL/PLA solution and then 5% resveratrol was added to fabricate a 3D scaffold via the TIPS technique. The prepared scaffolds were evaluated regarding their porosity, morphology, contact angle, degradation properties, biomechanical, blood compatibility, and cell viability via MTT assay. The scaffolds were further investigated by implantation in a rat femur defect model. PCL/PLA/GNF with 5% Resv showed a cancellated structure with irregular-shaped pores. The mean pore size was estimated to be 160 [Formula: see text]m and the porosity was 80.56 ± 2.68%. The contact angle of the fabricated scaffold was 95.4 ± 3.4, which determines the hydrophobic nature of the scaffold. Increased cell viability in scaffolds was observed by adding resveratrol. Twelve weeks after the implantation of the scaffold into the bone defect, the defects filled with PCL/PLA/GNF-resveratrol contained scaffold were remarkably better than PCL/PLA/GNF and negative control group (89.23 ± 6.34% in 12 weeks), and the difference was significant (p ¡ 0.05). In conclusion, the PCL/PLA/GNF scaffold containing 5% of resveratrol demonstrated adequate mechanical and physical properties. There is possible applicability of PCL/PLA/GNF scaffold containing 5% of resveratrol for surgical treatment of bone defects.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AUTOMATIC POLYP SEMANTIC SEGMENTATION USING WIRELESS CAPSULE ENDOSCOPY IMAGES WITH VARIOUS CONVOLUTIONAL NEURAL NETWORK AND OPTIMIZATION TECHNIQUES: A COMPARISON AND PERFORMANCE EVALUATION 基于各种卷积神经网络和优化技术的无线胶囊内窥镜图像息肉语义自动分割:比较和性能评价

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-09-30 DOI: 10.4015/s1016237223500266

Jothiraj Selvaraj, A. K. Jayanthy

{"title":"AUTOMATIC POLYP SEMANTIC SEGMENTATION USING WIRELESS CAPSULE ENDOSCOPY IMAGES WITH VARIOUS CONVOLUTIONAL NEURAL NETWORK AND OPTIMIZATION TECHNIQUES: A COMPARISON AND PERFORMANCE EVALUATION","authors":"Jothiraj Selvaraj, A. K. Jayanthy","doi":"10.4015/s1016237223500266","DOIUrl":"https://doi.org/10.4015/s1016237223500266","url":null,"abstract":"Colorectal cancer (CRC), ranking third most prevalent cancer type, can be diagnosed with the detection of polyps in the colon and rectum through endoscopic procedures facilitating prompt treatment. During visualization of gastrointestinal tract by the physician, there is high probability of miss rates and reviewing of the images is laborious. Automatic segmentation and detection are enabled with the convolutional neural networks (CNN). We segmented the polyps from the wireless capsule endoscopy images of Kvasir dataset using various CNN models. We have presented nine optimizers for each architecture and evaluated the performance parameters. The optimizers were graded based on the performance metrics in order to provide an insight for the researchers on the selection of optimizer and architecture. On comparison of the performance metrics of the pretrained and U-net-based architecture, the Adaptive Moment Estimation (ADAM) and Root Mean Squared Propagation (RMSPROP) optimizers received the highest score of 43 in the ranking, DiffGrad and Nesterov-accelerated Adaptive Moment Estimation (NADAM) ranked second with the score of 13, the Adaptive Delta (ADADELTA) ranked third with a score of 2, whereas Stochastic Gradient Descent (SGD), Adaptive Gradient Descent (ADAGRAD), and Adaptive Max (ADAMAX) optimizers performed least in the evaluation. Based on the deep learning application, the optimizer employed varies by considering computational speed, memory and computational time. This preliminary research provides the necessary key information for consideration in the development of an architecture with utilization of an optimizer.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136342558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EVALUATION OF THE EFFECTS OF LUNGS CHEST X-RAY IMAGE FUSION WITH ITS WAVELET SCATTERING TRANSFORM COEFFICIENTS ON THE CONVENTIONAL NEURAL NETWORK CLASSIFIER ACCURACY DURING THE COVID-19 DISEASE 新冠肺炎期间肺胸部x线图像融合及其小波散射变换系数对常规神经网络分类器准确率的影响

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-09-27 DOI: 10.4015/s1016237223500199

Roghayyeh Arvanaghi, Saeed Meshgini

{"title":"EVALUATION OF THE EFFECTS OF LUNGS CHEST X-RAY IMAGE FUSION WITH ITS WAVELET SCATTERING TRANSFORM COEFFICIENTS ON THE CONVENTIONAL NEURAL NETWORK CLASSIFIER ACCURACY DURING THE COVID-19 DISEASE","authors":"Roghayyeh Arvanaghi, Saeed Meshgini","doi":"10.4015/s1016237223500199","DOIUrl":"https://doi.org/10.4015/s1016237223500199","url":null,"abstract":"Background and Objective: Regarding the Coronavirus disease-2019 (COVID-19) pandemic in past years and using medical images to detect it, the image processing of the lungs and enhancement of its quality are some of the challenges in the medical image processing field. As it sounds from previous studies, the lung image processing has been raised in the other lung diseases such as lung cancer, too. Thus, the accurate classifying between normal lung image and abnormal is a challenge to aid physicians. Methods: In this paper, we have proposed an image fusion technique to increase the accuracy of classifier. In this technique, some signal preprocessing tools like discrete wavelet transform (DWT), wavelet scattering transform (WST), and image fusion by using DWT are employed to enhance ordinary convolutional neural network (CNN) classifier accuracy. Results: Unlike other studies, in this paper, different aspects of an image are fused with itself to emphasize its information which may be neglected in a total assessment of the image. We have achieved 89.8% accuracy for very simple structure of CNN classifier without using proposed fusion, and when we used proposed methods, the classifier accuracy increased to 91.8%. Conclusions: This study reveals using efficient preprocessing and presenting input images which lead to decrease the complications of deep learning classifier, and increase its accuracy overall.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"40 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135538455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DCAE-UNET: IMPROVED OPTIC DISC SEGMENTATION MODEL USING SEMI-SUPERVISED DEEP DILATED CONVOLUTION AUTOENCODER-BASED MODIFIED U-NET Dcae-unet:基于半监督深度扩张卷积自编码器的改进u-net视盘分割模型

IF 0.9

Biomedical Engineering: Applications, Basis and Communications Pub Date : 2023-08-31 DOI: 10.4015/s1016237223500254

R. Shalini, V. Gopi

{"title":"DCAE-UNET: IMPROVED OPTIC DISC SEGMENTATION MODEL USING SEMI-SUPERVISED DEEP DILATED CONVOLUTION AUTOENCODER-BASED MODIFIED U-NET","authors":"R. Shalini, V. Gopi","doi":"10.4015/s1016237223500254","DOIUrl":"https://doi.org/10.4015/s1016237223500254","url":null,"abstract":"An accurate assessment of the morphological characteristics of the Optic Disc (OD) is essential for the diagnosis of various retinal disorders. It is necessary to segment the OD precisely to detect structural OD changes associated with visual field loss. Although deep learning models are effective for this task, they require extensive labeled datasets, which can be time-consuming and costly. Furthermore, fundus images have multi-scale features, making segmentation challenging. In this study, we present a semi-supervised and transfer learning approach for OD segmentation. Our approach utilizes an im-proved Dilated Convolutional AutoEncoder (DCAE) and a pre-trained modified U-Net to segment the OD. The DCAE seg-ments the OD using feature similarity from unlabeled images in the Messidor dataset and saves the learned weights. Trans-fer learning is then applied to reuse the model weights in the U-Net, accelerating training on small datasets such as Drions-DB and Drishti-GS. The network architecture was modified by increasing the layers from 8 to 128 and halving the feature map length and width. To address the multi-scale challenge without inflating the model parameters, we introduce the Dilated Hierarchical Feature Extraction Module (DHFEM), a convolutional module capable of achieving multi-scale feature extraction without increasing model parameters. Additionally, DHFEM incorporates convolutional layers with varying recep-tive fields, further enhancing the network ability to extract features across multiple scales. Our OD segmentation method outperforms existing algorithms with reduced parameter quantities of 0.4 M. The mean Intersection over Union (mIoU) values are 0.9383 and 0.9629 and inference times of 45 ms and 40 ms for the Drions-DB and Drishti-GS datasets, respectively.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"49 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76796580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0