Image-based Detection of Dyslexic Readers from 2-D Scan path using an Enhanced Deep Transfer Learning Paradigm

Dyslexia is a learning syndrome commonly found in children that causes poor reading and comprehending skills even though they have normal intelligence. Dyslexia is more prevalent among school children. Dyslexia is caused by wide range of features and the exact cause is still unclear which makes it difficult for developing a generalized dyslexia detection model. Feature engineering to extract major features that contribute for generalized capability of the classifier is a significant challenge while developing a classification model for dyslexia. Conventional models for prediction of dyslexia based on psychological assessments, Imaging methods such as Magnetic Resonance Images, functional MRI images and Electroencephalogram (EEG) signals are not usually preferred for clinical disorders such as dyslexia especially on children due to adverse radioactive effects. To overcome these problems, this research work adapts an image-based technique for prediction of dyslexia based on eye gaze points while reading. Eye movement tracking methods are non-invasive and rich indices of brain study and cognitive processing. The eye gaze point while reading is tracked and represented as 2-D scan path images. The work also proposes an enhanced Dense Net deep transfer learning solution for feature engineering and classification of dyslexia. A new approach of enhanced Dense Net deep transfer learning is proposed where a deep learning model is built from 2d-scanpath images of dyslexia. This pre-trained model is used further to classify dyslexia using deep transfer learning. The proposed system uses the key characteristics of deep learning and transfer learning and has shown high performance when compared to existing state-of-the-art machine learning models with a high accuracy rate of 96.36 %. The results demonstrate that the enhanced deep transfer learning model performed well in identifying significant features and classification of dyslexia using 2-D scan path images.