Abstract
This paper presents the development of an Artificial Neural Network (ANN) model for pattern recognition for Chronic Obstructive Pulmonary Disease (COPD) diagnosis. Recent advancements in healthcare devices and the availability of numerous medical data have facilitated the management of chronic diseases. In addition, machine learning tools have made the management and diagnostic of a chronic illness more efficient by converting collected medical data from biosensors into meaningful clinical information. However, securing sensitive medical data, collected from patients, is still a challenging task. Hardware-based neural networks address this data safety concern by on-chip processing of acquired data, without cloud communications. Therefore, the presented ANN model was designed to comply with the intrinsic structure of neuromorphic platforms for future integrations.
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Soltani Zarrin, P., Wenger, C. (2019). Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-Based Neuromorphic Platforms. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_29
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DOI: https://doi.org/10.1007/978-3-030-30493-5_29
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