Abstract: A respiratory status classifying method is for classifying as one of at least two respiratory statuses and includes an original physiological parameter inputting step, an original chest image inputting step, a characteristic physiological parameter generating step, a characteristic chest image generating step, a training step and a classifier generating step. The characteristic chest image generating step includes processing at least a part of the original chest images, segmenting images of a left lung, a right lung and a heart from each of the original chest images that are processed, and enhancing image data of the images being segmented, so as to generate a plurality of characteristic chest images. The training step includes training two respiratory status classifiers using a plurality of characteristic physiological parameters and the characteristic chest images by at least one machine learning algorithm.

Abstract: A respiratory status classifying method is for classifying as one of at least two respiratory statuses and includes a training's physiological parameter inputting step, a training's chest image inputting step, a characteristic physiological parameter generating step, a characteristic chest image generating step, a training step and a classifier generating step. The characteristic chest image generating step includes processing at least a part of the training's chest images, segmenting images of a left lung, a right lung and a heart from each of the training's chest images that are processed, and enhancing image data of the images being segmented, so as to generate a plurality of characteristic chest images. The training step includes training a plurality of characteristic physiological parameters and the characteristic chest images by at least one machine learning algorithm.

Abstract: An image recognition method and an electronic apparatus configured for image recognition are provided. A training sample set is provided to train a recognition model including neural networks to recognize a classification label to which an image to be tested belongs through the trained recognition model. The training sample set includes image sets respectively belonging to users. During the training process, training images corresponding to classification labels are obtained from a first image set in the training sample set as reference images for training; a training image is obtained from a second image set different from the first image set as an input image for training; the reference images for training and the input image for training are obtained as inputs to the neural networks for training. The input to each neural network includes at least one of the reference images for training and the input image for training.

Abstract: A medical care system for assisting multi-diseases decision-making and real-time information feedback with artificial intelligence technology provided by the invention is capable of obtaining N training models respectively by mathematically operating M different diseases correspondingly based on a batch of collected medical information, obtaining inference results related to at least two diseases by inputting a single patient's data into all or part of the training models to perform mathematical calculation, at the same time, receiving feedback from professionals on the inference results to effectively integrate objective medical data of the patient with subjective medical data of the professionals, and constructing a multi-diseases data model based on the integrated data to be used as a tool for assisting multi-diseases decision-making.