Abstract: A retinal layer quantification system includes an image capturing unit and a processor electrically connected to the image capturing unit. The image capturing unit is configured to capture a target optical coherence tomographic image of a subject. The processor stores a program including a target image pre-processing module, a retinal layer auto-segmentation model, a target image enhancement module, a layer thickness detection module and a layer area detection module. The program detects a retinal layer thickness and a retinal layer area of the subject when the program is executed by the processor.

Abstract: An establishing method of a retinal layer thickness detection model includes following steps. A reference database is obtained, and an image pre-processing step, a feature selecting step, a training step and a confirming step are performed. The reference database includes reference optical coherence tomographic images. In the image pre-processing step, the reference optical coherence tomographic images are duplicated and cell segmentation lines of retinal layers are marked to obtain control optical coherence tomographic images. In the feature selecting step, the reference optical coherence tomographic images are analyzed to obtain reference image features. The training step is to train with the reference image features and obtain the retinal layer thickness detection model.

Abstract: An establishing method of a retinal layer thickness detection model includes following steps. A reference database is obtained, and an image pre-processing step, a feature selecting step, a training step and a confirming step are performed. The reference database includes reference optical coherence tomographic images. In the image pre-processing step, the reference optical coherence tomographic images are duplicated and cell segmentation lines of retinal layers are marked to obtain control optical coherence tomographic images. In the feature selecting step, the reference optical coherence tomographic images are analyzed to obtain reference image features. The training step is to train with the reference image features and obtain the retinal layer thickness detection model.

Abstract: A machine status and user behavior analysis system includes a detection side device and an analysis side device. The detection side device is electrically connected with the machine. The detection side device includes a data retrieving module, a sensing module, and a data processing module. The data retrieving module retrieves internal data of the machine. The sensing module senses the status of the machine to obtain sensed data. The data processing module processes the internal data of the machine and the sensed data to classify the internal data of the machine and the sensed data into machine status data and operation condition data. The analysis side device is wirelessly connected with the detection side device to receive the machine status data and the operation condition data. The analysis side device includes a data recognizing module which recognizes the machine status data to find a machine abnormal status and recognizes the operation condition data to find user behavior.