Biomedical Engineering: Applications, Basis and Communications最新文献

{"title":"SEGAN-BASED LESION SEGMENTATION AND OPTIMIZED RideNN FOR DIABETIC RETINOPATHY CLASSIFICATION","authors":"Vidya Sagvekar, Manjusha S. Joshi","doi":"10.4015/s1016237223500084","DOIUrl":"https://doi.org/10.4015/s1016237223500084","url":null,"abstract":"The most significant issue with diabetes is diabetic retinopathy (DR), which is the primary cause of blindness. DR typically develops no symptoms at the beginning of the disease, thus numerous physical examinations, including pupil dilation and a visual activity test, are necessary for DR identification. Due to the differences and challenges of DR, it is more challenging to identify it during the manual assessment. For DR patients, visual loss is prevented thanks to early detection and accurate therapy. Therefore, it is even more necessary to classify the severity levels of DR in order to provide a successful course of treatment. This study develops a deep learning method based on chronological rider sea lion optimization (CRSLO) for the classification of DR. The segmentation process divides the image into multiple subgroups, which is necessary for the appropriate detection and classification procedure. For the efficient identification of DR and classification of DR severity, the deep learning approach is used. Additionally, the CRSLO scheme is used to train the deep learning technique to achieve higher performance. With respect to testing accuracy, sensitivity, and specificity of 0.9218, 0.9304 and 0.9154, the newly introduced CRSLO-based deep learning approach outperformed other existing DR classification techniques like convolutional neural networks (CNNs), deep convolutional neural network (DCNN), synergic deep learning (SDL), HPTI-V4 and DR[Formula: see text]GRADUATE. The Speech Enhancement Generative Adversarial Network (SEGAN) model in use also produced increased segmentation accuracy of 0.90300.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"204 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76054791","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}

{"title":"DESIGN OF PATIENT SPECIFIC HIP PROSTHESIS BASED ON FINITE ELEMENT ANALYSIS: A COMPARATIVE STUDY","authors":"U. Snekhalatha, Raja Dhason, T. Rajalakshmi","doi":"10.4015/s1016237223500175","DOIUrl":"https://doi.org/10.4015/s1016237223500175","url":null,"abstract":"This study aims to develop a patient-specific hip implant for osteoarthritis conditions and to compare with intact and conventional implant. The femoral bone with head and shaft region was segmented from the pelvic griddle and converted into 3D model. The parameters such as femoral ball diameter, shaft length, acetabular cup diameter, and neck angle were measured from the segmented 3D model. In this study, designed part of hip implant was assembled together to form a customized hip implant. The von Mises stress was measured by means of Finite element analysis (FEA) method by applying various forces applied at the distal end of hip implant. The forces applied at hip implant were based on the assumption of 500 N force for standing, 2000 N force for walking, and 3000 N force for jogging condition. The minimum stress attained at the femur bone of custom-model is 1.32 MPa for 500 N loading condition, 5.3 MPa for 2000 N and 7.96 MPa for the maximum load of 3000 N. Thus the customized model experienced better stress distribution compared to conventional model under the maximum load of 3000 N. In pelvic region, the custom model attained a lower stress of 23% compared to conventional model. Thus, the study recommends the customized hip implants for the osteoarthritis conditions to avoid revision surgery.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"88 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72972302","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}

{"title":"SPACE CLOSURE EFFECT ON FORCE SYSTEM IN THE SEGMENTED ARCH: AN EXPERIMENTAL-NUMERICAL STUDY","authors":"Fábio Rodrigo Mandello Rodrigues, M. Ferreira, S. Ignácio, M. Luersen, P. Borges","doi":"10.4015/s1016237223500072","DOIUrl":"https://doi.org/10.4015/s1016237223500072","url":null,"abstract":"Objective: To perform an experimental-numerical analysis to study the influence of the interbracket distance (IBD) on the spring’s mechanical behavior and on the resulting force system during space closure in the segmented arch technique (SAT). Material and Methods: Twenty delta springs (DSs) made of beta-titanium alloy, [Formula: see text] inch, were tested on a platform transducer. A Young’s modulus ([Formula: see text] of 69 GPa ([Formula: see text] psi) and Yield’s strength ([Formula: see text] of 1240 MPa ([Formula: see text] psi) were used. The springs were activated considering different IBDs. The spring was modeled in autodesk Inventor software and its behavior was simulated using the finite element (FE) code Ansys Workbench. Results: The ANOVA showed a significant difference in the studied variables with a reliability of over 95% (only for the activation variable there was an effect upon the horizontal forces (Fx). The Tukey HSD and the Games–Howell post hoc multiple comparisons tests were applied to identify differences between the treatments for heterogeneous variances. Conclusions: The IBDs do not significantly affect the force system during space closure, even though there was an increase in the Mz/Fx ratio as spring deactivates. Activation can cause a statistically significant effect on the force system even though the force showed safe levels. At 4[Formula: see text]mm activation (19[Formula: see text]mm IBD), the spring wire starts yielding, i.e. plastic deformation occurs near the anterior attachment due to the shorter IBD.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"10 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82859383","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}

{"title":"CANCER PREDICTION IN INFLAMMATORY BOWEL DISEASE PATIENTS BY USING MACHINE LEARNING ALGORITHMS","authors":"F. Moayedi, J. Karimi, Seyed Ebrahim Dashti","doi":"10.4015/s1016237223500114","DOIUrl":"https://doi.org/10.4015/s1016237223500114","url":null,"abstract":"Colon cancer is one of the most common spread cancers in the world, which leads to total death of 10%. Prediction of onset of cancer, and the cause of its development in these patients can be of an enormous help and relief to those affected, as they can get back their “normal” life. Data mining and machine learning are important intelligent tools for classification, prediction and hidden relation extraction between patient information. We collected data from Shahid Faghihi Hospital in Shiraz. Features collected are as follows: Gender, age, duration of cancer before surgery, number of times the patients used bathroom, taking anti-inflammatory drug prednisolone, duration of drug use and dosage, kind of surgery and number of times consulted and retreatment of surgery, incontinence, etc. After pre-processing and data cleaning stages, effective features were extracted, and also occurrence of cancer predicts by using different classification algorithms. Then association rule mining algorithms like Apriori were used for obtaining any internal hidden relation between entries. Approaching them with different algorithms and assessing them with support vector machine was with highest prediction accuracy (84%). Due to unbalanced dataset, we chose cost sensitive support vector machine. In another aspect, after applying Apriori algorithm, the conditions of non-inflammation were extracted based on dataset features. Some significant outcomes are in what follows. If surgery treatment or diagnosed was less than 5 years, the possibility of developing colon cancer is lower. Also, as the duration of disease increases, the possibility of reoperation increases, as confirmed by the interiors. Since this issue with these features was raised for the first time in this paper at the suggestion of internists, early detection of cancer and also the extraction of effective laws can be of help to the medical community. In future, to get higher accuracy, the improvement of the dataset in terms of number of samples and colonoscopy image features is considered.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"25 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89059771","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}

{"title":"EoG COMMUNICATION SIGNAL FOR SLEEP LEVEL DETECTION","authors":"Nabil K. Al Shamaa, R. A. Fayadh, M. Wali","doi":"10.4015/s1016237223500102","DOIUrl":"https://doi.org/10.4015/s1016237223500102","url":null,"abstract":"The detection of sleep is important because it contributes to most road accidents especially high levels of deep sleep while driving. Sleep detection is based on electrooculogram (EoG) signal as sleep causes various changes to this signal. Drivers travelling for long hours, especially those working under transportation field are more likely to sleep in the middle of their journey. In order to avoid this situation, drivers are aided with a system which is capable of monitoring the drivers’ condition depending on communication between the driving simulator and the subject EoG signal as many sleep detection devices are dependent upon eye behavior and movement in addition to pupil size and eye closure for certain periods. Therefore, to solve the problem of detecting sleep while driving, this work extracted different features from the EoG signal precisely from its frequency range (0–25[Formula: see text]Hz) and (25–37.5[Formula: see text]Hz) by discrete wavelet transform technique. In this research, 15 subjects have been set in a driving environment for more than 1[Formula: see text]h for collecting the sleep EoG signal data by low power sensors. The EoG signal is recorded using Cobra3 Data acquisition set and few features (minimum, maximum, mean, standard deviation (SD), mode, energy, median and variance) are extracted using discrete wavelet transform. These features have been used to classify three classes (sleep 0, sleep 0, sleep 1) using support vector machine (SVM). This classifier depends upon the fusion of the above features to get an accuracy of 78% for high-level sleep detection based on db4 wavelet.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"14 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77442336","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}

{"title":"DUAL-SCALE CNN ARCHITECTURE FOR COVID-19 DETECTION FROM LUNG CT IMAGES","authors":"Alka Singh, V. Gopi, Anju Thomas, Omkar Singh","doi":"10.4015/s1016237223500126","DOIUrl":"https://doi.org/10.4015/s1016237223500126","url":null,"abstract":"Coronavirus Disease 2019 (COVID-19) is a terrible illness affecting the respiratory systems of animals and humans. By 2020, this sickness had become a pandemic, affecting millions worldwide. Prevention of the spread of the virus by conducting fast tests for many suspects has become difficult. Recently, many deep learning-based methods have been developed to automatically detect COVID-19 infection from lung Computed Tomography (CT) images of the chest. This paper proposes a novel dual-scale Convolutional Neural Network (CNN) architecture to detect COVID-19 from CT images. The network consists of two different convolutional blocks. Each path is similarly constructed with multi-scale feature extraction layers. The primary path consists of six convolutional layers. The extracted features from multipath networks are flattened with the help of dropout, and these relevant features are concatenated. The sigmoid function is used as the classifier to identify whether the input image is diseased. The proposed network obtained an accuracy of 99.19%, with an Area Under the Curve (AUC) value of 0.99. The proposed network has a lower computational cost than the existing methods regarding learnable parameters, the number of FLOPS, and memory requirements. The proposed CNN model inherits the benefits of densely linked paths and residuals by utilizing effective feature reuse methods. According to our experiments, the proposed approach outperforms previous algorithms and achieves state-of-the-art results.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"45 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81263637","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}

{"title":"EFFECTIVENESS OF LEARNING RATE IN DEMENTIA SEVERITY PREDICTION USING VGG16","authors":"Farhad Abedinzadeh Torghabeh, Yeganeh Modaresnia, Mohammad Mahdi khalilzadeh","doi":"10.4015/s1016237223500060","DOIUrl":"https://doi.org/10.4015/s1016237223500060","url":null,"abstract":"Alzheimer’s disease (AD) is the leading worldwide cause of dementia. It is a common brain disorder that significantly impacts daily life and slowly progresses from moderate to severe. Due to inaccuracy, lack of sensitivity, and imprecision, existing classification techniques are not yet a standard clinical approach. This paper proposes utilizing the Convolutional Neural Network (CNN) architecture to classify AD based on MRI images. Our primary objective is to use the capabilities of pre-trained CNNs to classify and predict dementia severity and to serve as an effective decision support system for physicians in predicting the severity of AD based on the degree of dementia. The standard Kaggle dataset is used to train and evaluate the classification model of dementia. Synthetic Minority Oversampling Technique (SMOTE) tackles the primary problem with the dataset, which is a disparity across classes. VGGNet16 with ReduceLROnPlateau is fine-tuned and assessed using testing data consisting of four stages of dementia and achieves an overall accuracy of 98.61% and a specificity of 99% for a multiclass classification, which is superior to current approaches. By selecting appropriate Initial Learning Rate (ILR) and scheduling it during the training phase, the proposed method has the benefit of causing the model to converge on local optimums with better performance.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136314559","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}

{"title":"HYBRID AI MODEL FOR THE DETECTION OF RHEUMATOID ARTHRITIS FROM HAND RADIOGRAPHS","authors":"R. Ahalya, U. Snekhalatha, Palani Thanaraj Krishnan","doi":"10.4015/s1016237223500096","DOIUrl":"https://doi.org/10.4015/s1016237223500096","url":null,"abstract":"The study aims to develop a computerized hybrid model using artificial intelligence (AI) for the detection of rheumatoid arthritis (RA) from hand radiographs. The objectives of the study include (i) segmentation of proximal interphalangeal (PIP), and metacarpophalangeal (MCP) joints using the deep learning (DL) method, and features are extracted using handcrafted feature extraction technique (ii) classification of RA and non-RA participants is performed using machine learning (ML) techniques. In the proposed study, the hand radiographs are resized to [Formula: see text] pixels and pre-processed using the various image processing techniques such as sharpening, median filtering, and adaptive histogram equalization. The segmentation of the finger joints is carried out using the U-Net model, and the segmented binary image is converted to gray scale image using the subtraction method. The features are extracted using the Harris feature extractor, and classification of the proposed work is performed using Random Forest and Adaboost ML classifiers. The study included 50 RA patients and 50 normal subjects for the evaluation of RA. Data augmentation is performed to increase the number of images for U-Net segmentation technique. For the classification of RA and healthy subjects, the Random Forest classifier obtained an accuracy of 91.25% whereas the Adaboost classifier had an accuracy of 90%. Thus, the hybrid model using a Random Forest classifier can be used as an effective system for the diagnosis of RA.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"3 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82836287","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}

{"title":"A NEW COMPUTER-AIDED DIAGNOSIS OF PRECISE MALARIA PARASITE DETECTION IN MICROSCOPIC IMAGES USING A DECISION TREE MODEL WITH SELECTIVE OPTIMAL FEATURES","authors":"Thanakorn Phumkuea, Phurich Nilvisut, T. Wongsirichot, Kasikrit Damkliang","doi":"10.4015/s1016237223500047","DOIUrl":"https://doi.org/10.4015/s1016237223500047","url":null,"abstract":"Malaria is a life-threatening mosquito-borne disease. Recently, the number of malaria cases has increased worldwide, threatening vulnerable populations. Malaria is responsible for a high rate of morbidity and mortality in people all around the world. Each year, many people, die from this disease, according to the World Health Organization (WHO). Thick and thin blood smears are used to determine parasite habitation and computer-aided diagnosis (CADx) techniques using machine learning (ML) are being used to assist. CADx reduces traditional diagnosis time, lessens socio-economic impact, and improves quality of life. This study develops a simplified model with selective features to reduce processing power and further shorten diagnostic time, which is important to resource-constrained areas. To improve overall classification results, we use a decision tree (DT)-based approach with image pre-processing called optimal features to identify optimal features. Various feature selection and extraction techniques are used, including information gain (IG). Our proposed model is compared to a benchmark state-of-art classification model. For an unseen dataset, our proposed model achieves accuracy, precision, recall, F-score, and processing time of 0.956, 0.949, 0.964, 0.956, and 9.877 s, respectively. Furthermore, our proposed model’s training time is less than those of the state-of-the-art classification model, while the performance metrics are comparable.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"58 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84009342","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}

{"title":"DETECTION AND CLASSIFICATION OF COVID-19 CASES FROM OTHER CARDIOVASCULAR CLASSES FROM ELECTROCARDIOGRAPHY SIGNALS USING DEEP LEARNING AND ResNet NETWORK","authors":"Shokufeh Akbari, Faraz Edadi Ebrahimi, Mehdi Rajabioun","doi":"10.4015/s1016237223500059","DOIUrl":"https://doi.org/10.4015/s1016237223500059","url":null,"abstract":"Nowadays, the world confronts a highly infectious pandemic called coronavirus (COVID-19) and over 4 million people worldwide have now died from this illness. So, early detection of COVID-19 outbreak and distinguishing it from other diseases with the same physical symptoms can give enough time for treatment with true positive results and prevent coma or death. For early recognition of COVID-19, several methods for each modality are proposed. Although there are some modalities for COVID-19 detection, electrocardiography (ECG) is one of the fastest, the most accessible, the cheapest and the safest one. This paper proposed a new method for classifying COVID-19 patients from other cardiovascular disease by ECG signals. In the proposed method, Resnet50v2 which is a kind of convolutional neural network, is used for classification. In this paper because of image format of data, first data with image format are applied to the network and then for comparison, ECG images are changed to signal format and classification is done. These two strategies are used for COVID-19 classification from other cardiac abnormalities with different filter sizes and the results of strategies are compared with each other and other methods in this field. As it can be concluded from the results, signal-based data give better accuracy than image classification at best performance and it is better to change the image format to signals for classification. The second result can be found by comparing with other methods in this field, the proposed method of this paper gives better performance with high accuracy in COVID-19 classification.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"108 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79725804","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}