Abstract: An image sensor includes: a pixel array including a plurality of sensing pixels each configured to convert a received light signal into an electrical signal; a readout circuit configured to convert the electrical signals into image data and output the image data; and a bad pixel correction circuit configured to: input, to a neural network, first input data comprising a first cluster bad pixel of the image data in a first direction to generate a first corrected pixel data, and flip second input data comprising a second cluster bad pixel of the image data in a second direction to generate third input data, and input, to the neural network, the third input data to generate a second corrected pixel data.

Abstract: An image signal processor, an image sensor, and an operating method of the image sensor are provided. The image sensor may include a pixel array including a plurality of sensing pixels each configured to convert a received light signal into an electrical signal, a readout circuit configured to convert the electrical signals into image data and output the image data, and an image signal processor configured to correct bad pixels occurring in a first pixel group of the image data, based on a neural network, encode position information representing a position of at least one bad pixel of the bad pixels to generate position data, and provide the position data to a first middle layer of a plurality of layers included in the neural network.

Abstract: An image sensor includes: a pixel array including a plurality of sensing pixels each configured to convert a received light signal into an electrical signal; a readout circuit configured to convert the electrical signals into image data and output the image data; and a bad pixel correction circuit configured to: input, to a neural network, first input data comprising a first cluster bad pixel of the image data in a first direction to generate a first corrected pixel data, and flip second input data comprising a second cluster bad pixel of the image data in a second direction to generate third input data, and input, to the neural network, the third input data to generate a second corrected pixel data.

Abstract: Provided is a method for diagnosing a disease risk based on complex genetic information network analysis. In the method for diagnosing a disease risk based on complex genetic information network analysis according to the present invention, it is possible to deduce a stable correlation with a disease from a small number of genetic information combination by introducing an optimization method or learning method, and it is possible to provide a genetic information correlation based on a network model. A diagnosis technology satisfying accuracy and economical efficiency enough to be commercially used in an actual medical field by using the correlation between the genetic information and the disease deduced in the present invention will be secured. Further, the biomarker deduced in the present invention will be commercially used in manufacturing a medical device including a diagnosis chip and terminal and in disease diagnosis service.