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Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost-effectiveness, and low susceptibility to motion artifacts. However, the fNIRS-based computer-aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS-based CAD of MDD with high accuracy.
Pubmed ID: 40226684
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Deep learning framework, with support for JAX, TensorFlow, and PyTorch. Used to build and train models for computer vision, natural language processing, audio processing, timeseries forecasting, recommender systems. Offers consistent and simple APIs, minimizes number of user actions required for common use cases, and provides clear and actionable error messages. Keras also gives the highest priority to crafting documentation and developer guides.
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