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  1. Iza Sazanita Isa, Muhammad Aiman Zahir, Siti Azura Ramlan, Wang, Li-Chih, Siti Noraini Sulaiman
    ESTEEM Academic Journal, 2021;17(1):12-25.
    MyJurnal
    Dyslexia is developed by neurobiological in origin which is categorized as
    learning disorder that affect the ability to read, spell, write and speak. The
    most common dyslexia symptom can easily be identified through the
    handwriting pattern. There are many intelligence and computational
    methods that have been proposed, and they have provided various and
    different performance to evaluate the proposed system ability. However,
    system performances are varied and nonstandardized in each assesment on
    dyslexic children to validate the presence of dyslexia symptom. The recent
    deep learning models have been employed to improve the assesment
    performance and (the models/ they have shown) shows significant output to
    detect and classify the present of dyslexia symptoms among school children.
    Therefore, there is a crucial need in deep learning, specifically for
    Convolutional Neural Network ( CNN) to validate performances of different
    networks, so that the most performed CNN could be a bench mark in
    evaluation to detect such symptom. This study aims to compare different deep
    learning networks specifically the CNN models to validate its performance
    in terms of the capability to classify dyslexic handwriting among school
    children. This study is proposed to compare different CNN models such as
    CNN-1, CNN-2, CNN-3 and LeNet-5. The proposed methods to compare the
    CNN performances are developed by using Jupyter notebook as platform.
    Meanwhile, keras is the higher-level API framework to provide a more
    flexible way for defining models. It specifically allows to define multiple
    input or output models as well as models that share layers. The tensorflow is
    also used for machine learning applications such as neural networks. Before
    that, the dataset of handwriting image is preprocessed by the augmentation
    process which includes the rotation of all images. CNN models have shown
    significant performance and provided sufficient results of performance with
    more than 87% of accuracy in classifying the potential dyslexia symptom
    based on handwritten images.
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