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This paper introduces a novel low-cost device prototype for the automaticdiagnosis of diseases, utilizing inputted symptoms and personal background. Theengineering goal is to solve the problem of limited healthcare access with asingle device. Diagnosing diseases automatically is an immense challenge, owingto their variable properties and symptoms. On the other hand, Neural Networkshave developed into a powerful tool in the field of machine learning, one thatis showing to be extremely promising at computing diagnosis even withinconsistent variables.  In this research, a cheap device was created to allow for straightforwarddiagnosis and treatment of human diseases. By utilizing Deep Neural Networks(DNNs) and Convolutional Neural Networks (CNNs), outfitted on a Raspberry PiZero processor ($5), the device is able to detect up to 1537 different diseasesand conditions and utilize a CNN for on-device visual diagnostics. The user caninput the symptoms using the buttons on the device and can take pictures usingthe same mechanism. The algorithm processes inputted symptoms, providingdiagnosis and possible treatment options for common conditions. The purpose ofthis work was to be able to diagnose diseases through an affordable processorwith high accuracy, as it is currently achieving an accuracy of 90% for Top-5symptom-based diagnoses, and 91% for visual skin diseases. The NNs achieveperformance far above any other tested system, and its efficiency and ease ofuse will prove it to be a helpful tool for people around the world. This devicecould potentially provide low-cost universal access to vital diagnostics andtreatment options.

Low-Cost Device Prototype for Automatic Medical Diagnosis Using Deep Learning Methods