International Journal of Online and Biomedical Engineering最新文献

{"title":"Brain Tumor Classification Deep Learning Model Using Neural Networks","authors":"G. Maquen-Niño, Ariana Ayelen Sandoval-Juarez, Robinson Andres Veliz-La Rosa, Gilberto Carrión-Barco, Ivan Adrianzén-Olano, Hugo Vega-Huerta, Percy De-La-Cruz-VdV","doi":"10.3991/ijoe.v19i09.38819","DOIUrl":"https://doi.org/10.3991/ijoe.v19i09.38819","url":null,"abstract":"The timely diagnosis of brain tumors is currently a complicated task. The objective was to build an image classification model to detect the existence or not of brain tumors by adding a classification header to a ResNet-50 architecture. The CRISP-DM methodology was used for data mining. A dataset of 3847 brain MRI images was used, 2770 images for training, 500 for validation, and 577 for testing. The images were resized to a 256 × 256 scale and then a data generator is created that is responsible for dividing pixels by 255. The training was performed and then the evaluation process was carried out, obtaining an accuracy percentage of 92% and a precision of 94% in the evaluation process. It is concluded that the proposed CNN model composed of a head with a ResNet50 architecture and a seven-layer convolutional network achieves adequate accuracy, becoming an efficient and complementary proposal to other models developed in previous works.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42949744","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":"Prediction of Depression via Supervised Learning Models: Performance Comparison and Analysis","authors":"Zineb Sabouri, Noreddine Gherabi, Mohammed Nasri, Mohamed Amnai, Hakim El Massari, Imane Moustati","doi":"10.3991/ijoe.v19i09.39823","DOIUrl":"https://doi.org/10.3991/ijoe.v19i09.39823","url":null,"abstract":"This document Among all the various types of mental and psychosocial illnesses, the most commonly occurring type is depression. It can cause serious problems such as suicide. Therefore, early detection is important to stop the progression of this disease that could endanger human lives. Predicting and detecting early-stage depression using machine learning (ML) techniques is a promising strategy. This study’s main purpose is to assess which ML techniques are highly appropriate and accurate regarding such diagnoses. Six supervised ML techniques namely: K-nearest neighbor (KNN), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Support vector machine (SVM) and Naive Bayes (NB) were applied on dataset collected from Kaggle and compared for their accuracy (ACC) and performance in predicting depression. The performance of each model was evaluated using 10-fold cross-validation and evaluated in terms of ACC, F1-score, Precision (PR), and Sensitivity (SEN). Based on the experimental results analysis, we can conclude that SVM and LR performed better than all other methods with an ACC of 83,32%. Therefore, we found that a simple ML algorithm can be used to assist clinicians and practitioners predict depression at an early stage, with excellent potential utility and a considerable degree of ACC.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42895695","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":"Data Mining Application for the Spread of Endemic Butterfly Cenderawasih Bay using the K-Means Clustering Algorithm","authors":"F. Y. Wattimena, Abilliyo S. Mampioper, Reni Koibur, I. Nyoman G. A. Astawa, D. Novaliendry, Noper Ardi, N. Mahyuddin","doi":"10.3991/ijoe.v19i09.40907","DOIUrl":"https://doi.org/10.3991/ijoe.v19i09.40907","url":null,"abstract":"The superfamily Papilionoidea day butterfly, which is endemic to the Cenderawasih Bay islands (Numfor, Supiori, Biak and Yapen), consists of 6 family species: the Papilionidae, Hesperiidae, Pieridae, Riodinidae, Lycaenidae and Nymphalidae families. This study aims to analyze the grouping of endemic butterflies of the Bay of Cendrawasih based on wings and colours in 4 Clusters, namely Numfor, Supiori, Biak and Yapen Islands, by applying the function of the K-Means Clustering algorithm data mining method. The grouping selection was carried out 7 times with the conclusion that Numfor had 13 types of Endemic Butterfly species, Biak had 7 Papuan Endemic Butterfly Species, Supiori had 9 Endemic Butterfly Species, and Yapen had 11 Endemic Butterfly Species. The analysis results were then retested in an application built using the Waterfall system development method and the PHP and MySQL programming languages. In addition to applying the K-Means Clustering algorithm for grouping endemic butterflies, the application created produces a butterfly distribution map that displays butterfly information based on family.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48475040","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":"Mobile Learning in Medical Coding Course: Intention to Use MedCoS","authors":"Nor Intan Shamimi Abdul Aziz, Dilla Syadia Ab Latiff, Siti Noorsuriani Maon, Annurizal Anuar","doi":"10.3991/ijoe.v19i09.40913","DOIUrl":"https://doi.org/10.3991/ijoe.v19i09.40913","url":null,"abstract":"Medical coding is a subject in which students must assign proper ICD-10 codes to patients’ diagnoses as reported in the coding exercises. However, due to students’ inadequate knowledge, incorrect codes are assigned to the cases, leading to coding errors. Thus, creating Medical Coding Simulation (MedCoS) is to help students strengthen their motor and technical abilities in challenging scenarios. The purpose of this study is to predict students’ intention to use MedCoS based on attitudes (AT), subjective norms (SN), and perceived behavioral control (PBC). To meet the objective, SPSS was used to conduct descriptive, reliability, and multiple regression analyses. This study includes students in Semester five and six who have attended both courses. Majority respondents were female (89.9%, n=116) and aged between 23 and 24 years old (90.2%, n=102). Results showed that attitudes and perceived behavioral predicted the intention to use MedCos among the students. The significant outcome allows MedCoS to plan the next stage of the application’s development with the goal of achieving the desired improvement in course performance.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46777678","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 Context-Aware Framework to Manage the Priority of Injured Persons Arriving at Emergencies","authors":"Fathia Ouakasse, O. Stitini, S. Rakrak","doi":"10.3991/ijoe.v19i08.39197","DOIUrl":"https://doi.org/10.3991/ijoe.v19i08.39197","url":null,"abstract":"The integration of Internet of Medical Things (IoMT) in Hospital system has modified the traditional medical service as a reactive system based on hospitalization and diseases to a preventive and interoperable system based mainly on the interactive data flow between patient and health professionals. Using medical connected objects (MCOs), medical data is collected and processed. According to gathered data, the new medical system should be able to sort patient states based on urgent and critical vital signs, and consequently priorities are defined. In this paper, we direct our attention to manage priority in hospital emergencies in order to adapt dynamically operations and interactions with different stakeholders according to the changes in their execution context. Indeed, based on data sensed from MCOs implemented in ambulances, emergency rooms might be prepared to receive injured persons like victims of road accidents or other incidents. Therefore, we design a context-aware monitoring framework for injured people based on gathered medical data to manage priorities.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44487904","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":"Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs","authors":"P. Dodamani, A. Danti","doi":"10.3991/ijoe.v19i08.39235","DOIUrl":"https://doi.org/10.3991/ijoe.v19i08.39235","url":null,"abstract":"Osteoporosis is a condition that affects the entire skeletal system, resulting in decreased density of bone mass and the weakening of bone tissue's micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50 and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of Spine, Hand, Leg, Knee, and Hip, comprising of Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model, and the findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% with Achieving an error rate of 0.07 and a validation loss of only 0.57 in comparison with other models considered in this study.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45800893","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":"The Performance of Sensitivity-Maps Method in Reconstructing Low Contrast and Multi-Contrast Objects for Microwave Imaging Applications","authors":"Basari, Syahrul Ramdani","doi":"10.3991/ijoe.v19i08.38665","DOIUrl":"https://doi.org/10.3991/ijoe.v19i08.38665","url":null,"abstract":"The microwave imaging system for breast tumor/cancer detection requires high sensitivity to detect abnormal tissue that has little contrast in high-density breasts. This paper proposes a qualitative microwave imaging system simulation based on inverse scattering using the sensitivity-maps method. This method utilizes two measurement types for system calibration: a reference object as a scatterer-free background and a calibration object to obtain the system's impulse response. The object under test (OUT) consists of an object with low dielectric contrast and a phantom with multiple low dielectric contrasts (multi-contrast). Reconstruction is carried out on three types of S-parameter measurement data, namely S_11,〖 S〗_21, and a combination of both. S-parameters are measured at several frequencies, which are 3, 10, 14, 15, 16, 20 GHz, and the combination of all those frequencies (multifrequency). Reconstructed images show that the system is capable of reconstructing dielectric objects accurately. Quantitatively, the results show that the multifrequency S_21 measurement yields the best image quality with relative root mean squared error (RRMSE) values of 0.1272 and structural similarity index (SSIM) of 0.9076. The designed imaging system also successfully reconstructs multi-contrast phantom accurately with RRMSE of 0.1434 and SSIM of 0.4609.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41957271","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":"An Efficient Preprocessing Technique for Multimodality Breast Cancer Images","authors":"A. Y. K., A. S, Ramesh Babu D. R.","doi":"10.3991/ijoe.v19i08.40043","DOIUrl":"https://doi.org/10.3991/ijoe.v19i08.40043","url":null,"abstract":"On average, one in every eight women is diagnosed with breast cancer during their lifetime, and accounts for 14% of cancers in women. Since early diagnosis could improve treatment outcomes and longer survival times for patients, it is absolutely necessary to develop techniques to classify lesions within breast cancer mammograms and ultrasound images. The main goal is to determine the class of tumor present within the image, which is pivotal in diagnosing breast cancer patients. In this paper, we propose an Sobel-Canny-Gabor(SCG) model, which is a hybrid model that implements three different edge detection filters; Sobel filter, Gabor filter, and Canny filter. This model is used to enhance the appearance of the mammogram and ultrasound images, which is then fed into a classification model. Through classification, there could be a potential improvement in the results of the overall classification. Post-classification, the model is then evaluated using the metric Peak Signal-to-Noise Ratio (PSNR), which measures the quality between the original image and the compressed image.On average, one in every eight women is diagnosed with breast cancer during their lifetime, and accounts for 14% of cancers in women. Since early diagnosis could improve treatment outcomes and longer survival times for patients, it is absolutely necessary to develop techniques to classify lesions within breast cancer mammograms and ultrasound images. The main goal is to determine the class of tumor present within the image, which is pivotal in diagnosing breast cancer patients. In this paper, we propose an Sobel-Canny-Gabor(SCG) model, which is a hybrid model that implements three different edge detection filters; Sobel filter, Gabor filter, and Canny filter. This model is used to enhance the appearance of the mammogram and ultrasound images, which is then fed into a classification model. Through classification, there could be a potential improvement in the results of the overall classification. Post-classification, the model is then evaluated using the metric Peak Signal-to-Noise Ratio (PSNR), which measures the quality between the original image and the compressed image.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45946979","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":"Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection","authors":"Tun Azshafarrah Ton Komar Azaharan, A. Mahamad, S. Saon, Muladi, S. Mudjanarko","doi":"10.3991/ijoe.v19i08.38619","DOIUrl":"https://doi.org/10.3991/ijoe.v19i08.38619","url":null,"abstract":"A brain tumor is a very common and devastating malignant tumor that leads to a shorter lifespan if not detected early enough. Brain tumor classification is a critical step after the tumor has been identified to create an effective treatment plan. This study aims to investigate the three deep learning tools, VGG-16 ResNet50 and AlexNet in order to detect brain tumor using MRI images. The results performance are then evaluated and compared using accuracy, precision and recall criteria. The dataset used contained 155 MRI images which are images with tumors, and 98 of them are non-tumors. The AlexNet model perform extremely well on the dataset with 96.10% accuracy, while VGG-16 achieved 94.16% and ResNet-50 achieved 91.56%. These accuracies positively impact the early detection of tumors before the tumor causes physical side effects such as paralysis and other disabilities.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48903051","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":"Burnout Prevalence in Special Education Teachers, and the Positive Role of ICTs","authors":"Agathi Stathopoulou, Despina Spinou, Anna-Maria Driga","doi":"10.3991/ijoe.v19i08.38509","DOIUrl":"https://doi.org/10.3991/ijoe.v19i08.38509","url":null,"abstract":"The aim of this study was to investigate special education teacher`s level of burnout. In particular, it sought to examine the role their personal characteristics play in the occurrence of the syndrome.  A quantitative research design was used to describe the association between the variables The data was collected using the Maslach Burnout Inventory for Education  (M.B.I.-E.S.)  consisted of three dimensions: Emotional exhaustion, Depersonalization, and Personal accomplishment. The sample consisted of 202 Special Education (S.E) teachers who completed the M.B.I.-E.S.  The results of this research showed that: a) the sample experiences burnout and special attention is required for the scale of emotional exhaustion b) age, school settings , specialty, and the total previous service with or without students with special educational needs (S.E.N.) were significantly correlated and  affected burnout dimensions  ","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47023130","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}