Automated Schizophrenia Detection from Handwriting Samples via Transfer Learning Convolutional Neural Networks

Abstract

Schizophrenia is a globally prevalent psychiatric disorder that severely impairs daily life. Schizophrenia is caused by dopamine imbalances in the fronto-striatal pathways of the brain, which influences fine motor control in the cerebellum. This leads to abnormalities in handwriting. The goal of this study was to develop an accurate, objective, and accessible computational method to be able to distinguish schizophrenic handwriting samples from non-schizophrenic handwriting samples. To achieve this, data from Crespo et al. (2019) was used, which contains images of handwriting samples from schizophrenic and non-schizophrenic patients. The data was preprocessed and augmented to produce a more robust model that can recognize different types of handwriting. The data was used to train several different convolutional neural networks, and the model with the base architecture of InceptionV3 performed the best, differentiating between the two types of image with a 92% accuracy rate. To make this model accessible, a secure website was developed for medical professionals to use for their patients. Such a result suggests that handwriting analysis through computational models holds promise as a non-invasive and objective method for clinicians to diagnose and monitor schizophrenia.