Abstract: The present invention relates to a uterine cervical image acquisition apparatus which acquires and displays images of a uterine cervix, and in some cases can automatically diagnose the onset of a disease related to the uterine cervix, the apparatus being characterized by comprising: a main body in which a camera for acquiring uterine cervical images is installed on one side, a user interface for displaying the uterine cervical images and inputting user touch commands is installed on the opposite side, and a handlebar unit is formed in a downward direction; a light source unit which emits light toward the front of the main body; a battery which is positioned inside the handlebar unit to supply power for charging and operation; an operation button unit which includes at least an image capturing button, a zoom-in button, and a zoom-out button formed on the handlebar unit; a memory for storing the captured uterine cervical images; a communication unit for transmitting the uterine cervical images to a reading comp

Abstract: The present invention relates to a system for diagnosing a lesion on an endoscopic image, and includes an observation image acquisition unit configured to acquire an observation image from an input endoscopic image, a pre-processing unit configured to pre-process an acquired observation image, a lesion diagnosis unit configured to diagnose a degree of lesion on the pre-processed observation image using a pre-trained artificial neural network learning model for lesion diagnosis, and a screen display control unit configured to display and output a lesion diagnosis result.

Abstract: The present invention relates to an ultrasound diagnostic system using an artificial neural network which is capable of providing convenience in operating a diagnostic device by performing guidance such that an ultrasound image of a carotid artery, a thyroid, a breast, a femoral vein, or a medium vein may be acquired at an optimal position. The ultrasound diagnostic system includes a diagnostic part search unit which finds a diagnostic part (carotid artery, thyroid, femoral vein, medium vein, or breast) from input images and is configured to represent and output at least the diagnostic part in a color differentiated from that of tissue, and an automatic diagnosis unit which diagnoses whether the diagnostic part is abnormal with respect to an image of the diagnostic part found by the diagnostic part search unit based on a first artificial neural network and is configured to output a diagnosis result.

Abstract: The present disclosure relates to a carotid artery diagnostic method, and more particularly to a method for diagnosing an abnormal symptom of a carotid artery using one or more artificial neural networks. A carotid ultrasound diagnostic method capable of being executed in a computer system capable of reading a carotid ultrasound video image includes extracting a carotid artery blood vessel image from an input or received carotid ultrasound video image or a carotid ultrasound video image accessed from a storage unit using a pre-trained artificial neural network, and diagnosing whether a carotid artery for the extracted carotid artery blood vessel image is normal using a pre-trained artificial neural network and displaying and outputting a diagnosis result.

Abstract: The present invention relates to a uterine cervical image acquisition apparatus which acquires and displays images of a uterine cervix, and in some cases can automatically diagnose the onset of a disease related to the uterine cervix, the apparatus being characterized by comprising: a main body in which a camera for acquiring uterine cervical images is installed on one side, a user interface for displaying the uterine cervical images and inputting user touch commands is installed on the opposite side, and a handlebar unit is formed in a downward direction; a light source unit which emits light toward the front of the main body; a battery which is positioned inside the handlebar unit to supply power for charging and operation; an operation button unit which includes at least an image capturing button, a zoom-in button, and a zoom-out button formed on the handlebar unit; a memory for storing the captured uterine cervical images; a communication unit for transmitting the uterine cervical images to a reading comp

Abstract: The present invention relates to an automatic cervical cancer diagnosis system for performing machine learning by classifying cervical data required for automatic diagnosis of cervical cancer according to accurate criteria and automatically diagnosing cervical cancer based on the machine learning, the automatic cervical cancer diagnosis system including: a learning data generator configured to classify unclassified photographed image data for a cervix transmitted from an external device or a storage according a combination of multi-level classification criteria to generate learning data for each new classification criterion in a learning mode; a photographed image pre-processer configured to pre-process photographed cervix images; a cervical cancer diagnoser including a machine learning model for cervical cancer that learns a characteristic of the learning data generated for each classification criterion in the learning mode, wherein the machine learning model generates diagnosis information about whether cer