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

本研究利用深度神经网络和卷积神经网络，设计了一种低成本的设备原型，能够通过输入症状和个人背景信息进行自动诊断，实现对多种疾病的有效诊断和治疗，准确率高达 90%。该工具能够为全球人们提供低成本的重要诊断和治疗手段。