Filters

2019 - 201912020 - 20242
Reset filters

Settings

Publication Index: PubMedSubject: classification

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    Citation - WoS: 5
    Citation - Scopus: 8
    A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification
    (Mdpi, 2024) Kadhim, Yezi Ali; Guzel, Mehmet Serdar; Mishra, Alok
    Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.
  • Thumbnail Image
    Article
    Citation - WoS: 25
    Citation - Scopus: 33
    Hybrid Eeg-Fnirs Bci Fusion Using Multi-Resolution Singular Value Decomposition (msvd)
    (Frontiers Media Sa, 2020) Khan, Muhammad Umer; Hasan, Mustafa A. H.
    Brain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system-achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals-is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.
  • Thumbnail Image
    Article
    Citation - WoS: 7
    Citation - Scopus: 7
    Classification of Intermediate and Novice Surgeons' Skill Assessment Through Performance Metrics
    (Sage Publications inc, 2019) Topalli, Damla; Cagiltay, Nergiz Ercil
    Background. Endoscopic surgeries have become an alternative for open procedures whenever possible. For such types of operations, surgeons are required to gain several skills, whose development needs hands-on practice. Accordingly, gaining these skills today is a challenge for surgical education programs. Despite the development of several technology-enhanced training environments, there are still problems to better integrate these technologies into educational programs. For an appropriate integration, it is critical to assess the skill levels and adapt the training content according to the trainees' requirements. In the literature, there exist several methods for assessing these skill levels. However, there are still problems in practice for objective and repetitive assessment. Methods. The present study aims to estimate the skill levels of participants in surgical training programs in an objective manner by collecting experimental data from residents in an endoscopic surgical simulation environment and gathering performance metrics. Results. It is shown that, by comparing the results of a number of classification algorithms for the best accuracy estimation and feature set, the "novice" and "intermediate" skill levels can be estimated with an accuracy of 86%. Conclusions. The outcomes help surgical educators and instructional system designers to better assess the skill levels of the trainees and guide them accordingly. In addition, objective assessments as highlighted in this study can be beneficial when designing technology-enhanced adaptive learning environments.