Classification of Dyslexia Among School Students Using Deep Learning

Abstract

Dyslexia is a common learning disorder that affects children’s reading and writing skills. Early identification of Dyslexia is essential for providing appropriate interventions and support to affected children. Traditional methods of diagnosing Dyslexia often rely on subjective assessments and the expertise of specialists, leading to delays and potential inaccuracies in diagnosis. This study proposes a novel approach for diagnosing dyslexic children using spectrogram analysis and convolutional neural networks (CNNs). Spectrograms are visual representations of audio signals that provide detailed frequency and intensity information. CNNs are powerful deep-learning models capable of extracting complex patterns from data. In this research, raw audio signals from dyslexic and non-dyslexic children are transformed into spectrogram images. These images are then used as input for a CNN model trained on a large dataset of dyslexic and non-dyslexic samples. The CNN learns to automatically extract discriminative features from the spectrogram images and classify them into dyslexic and non-dyslexic categories. This study’s results demonstrate the proposed approach’s effectiveness in diagnosing dyslexic children. The CNN accurately identified dyslexic individuals based on the spectrogram features, outperforming traditional diagnostic methods. Spectrograms and CNNs provide a more objective and efficient approach to dyslexia diagnosis, enabling earlier intervention and support for affected children. This research contributes to the field of dyslexia diagnosis by harnessing the power of machine learning and audio analysis techniques. Facilitating faster and more accurate identification of Dyslexia in children, ultimately improving their educational outcomes and quality of life.