A Model for Medical Diagnosis Based on Plantar Pressure
- Resource Type
- Conference
- Authors
- Xu, Guoxiong; Huang, Hongshi; Liu, Can; Wang, Zhengfei; Li, Wenxin; Liu, Shilei
- Source
- 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Advances in Pattern Recognition (ICAPR), 2017 Ninth International Conference on. :1-6 Dec, 2017
- Subject
- Computing and Processing
Geoscience
Robotics and Control Systems
Signal Processing and Analysis
Medical diagnosis
Diseases
Foot
Data models
Medical diagnostic imaging
Convolutional neural networks
Computational modeling
medical diagnosis
plantar pressure
representation learning
convolutional neural network
- Language
The process of determining which disease or condition explains a person's symptoms and signs can be very complicated and may be inaccurate in some cases. The general belief is that diagnosing diseases relies on doctors' keen intuition, rich experience and professional equipment. In this work, we employ ideas from recent advances in plantar pressure research and from the powerful capacity of the convolutional neural network for learning representations. Here, we propose a model using convolutional neural network based on plantar pressure for medical diagnosis. Our model learns a network that maps plantar pressure data to its corresponding medical diagnostic label. We then apply our model to make the medical diagnosis on datasets we collected from cooperative hospital and achieve an accuracy of 98.36%. We demonstrate that the model base on the convolutional neural network is competitive in medical diagnosis.