The phrase "automatic diagnosis systems" is correct and usable in written English.
It can be used in contexts related to technology, healthcare, or any field where systems are designed to diagnose issues automatically.
Example: "The development of automatic diagnosis systems has revolutionized the way medical professionals identify diseases."
Alternatives: "automated diagnostic systems" or "self-diagnosing systems".
The proposed tool was designed and is being developed with the support of two TCM therapists, which act as experts and provide aid to the processes of building the knowledge base and the automatic diagnosis system.
The goal is to detect the intersection points between the vessels, as they could provide useful information to an automatic diagnosis system.
In this paper, an automatic diagnosis recommender system for classifying leukemia based on cooperative game is presented.
Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.
The automatic reflective diagnosis system (ARDK) is a device that detects human bioenergy through measuring skin conductance at 24 special acupoints on the wrists and ankles.
The ECG is susceptible to noise and it is essential to remove the noise to support decision making for specialist and automatic heart disorder diagnosis systems.
In the field of self-healing, several research efforts have been devoted to the development of usable automatic detection and diagnosis systems [29].
With a high mixing efficiency and the advantage of being easy to fabricate, the STB micromixer can be utilized in various microfluidic, point-of-care, point-of-need, central automatic diagnosis, and pre-treatment systems including sensor control systems.
This paper presents an in-field automatic wheat disease diagnosis system based on a weakly supervised deep learning framework, i.e. deep multiple instance learning, which achieves an integration of identification for wheat diseases and localization for disease areas with only image-level annotation for training images in wild conditions.
So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale.
