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  1. AU="Mohd Khairuddin, Ismail"
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  1. Book ; Online ; E-Book: Deep Learning in Cancer Diagnostics

    Arzmi, Mohd Hafiz / Abdul Majeed, Anwar. P. P. / Muazu Musa, Rabiu / Mohd Razman, Mohd Azraai / Gan, Hong-Seng / Mohd Khairuddin, Ismail / Ab. Nasir, Ahmad Fakhri

    A Feature-based Transfer Learning Evaluation

    (SpringerBriefs in Forensic and Medical Bioinformatics,)

    2023  

    Abstract: Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries ...

    Author's details by Mohd Hafiz Arzmi, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Hong-Seng Gan, Ismail Mohd Khairuddin, Ahmad Fakhri Ab. Nasir
    Series title SpringerBriefs in Forensic and Medical Bioinformatics,
    Abstract Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.
    Keywords Medical physics ; Artificial intelligence ; Cancer/Imaging ; Computational intelligence ; Medical Physics ; Artificial Intelligence ; Cancer Imaging ; Computational Intelligence
    Subject code 610.153
    Language English
    Size 1 online resource (41 pages)
    Edition 1st ed. 2023.
    Publisher Springer Nature Singapore ; Imprint: Springer
    Publishing place Singapore
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 981-19-8937-0 ; 9789811989360 ; 978-981-19-8937-7 ; 9811989362
    DOI 10.1007/978-981-19-8937-7
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: The classification of movement intention through machine learning models: the identification of significant time-domain EMG features.

    Mohd Khairuddin, Ismail / Sidek, Shahrul Naim / P P Abdul Majeed, Anwar / Mohd Razman, Mohd Azraai / Ahmad Puzi, Asmarani / Md Yusof, Hazlina

    PeerJ. Computer science

    2021  Volume 7, Page(s) e379

    Abstract: Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have ... ...

    Abstract Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
    Language English
    Publishing date 2021-02-25
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.379
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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