An image analysis method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure , the method comprising: generating analysis data from an analysis target image that includes the tissue or the cell to be analyzed; inputting the analysis data to the deep learning algorithm ; and generating data indicating a region of a cell nucleus in the analysis target image by the deep learning algorithm , wherein: the deep learning algorithm is generated from training data including pairs of first training data and second training data , the first training data includes a color density value data of each pixel generated from a bright field image of a tissue or a cell which is stained for bright field observation, the second training data includes a binarized data of each pixel generated from a fluorescence image of a same or corresponding region of the tissue or the cell whose nucleus is stained for florescence observation, wherein the binarized data indicates whether each pixel is a region of a cell nucleus, and the deep learning algorithm learns a relationship between the first training data , as an input layer of the neural network structure , and the second training data , as an output layer of the neural network structure . generating analysis data from an analysis target image that includes the tissue or the cell to be analyzed; inputting the analysis data to the deep learning algorithm ; and generating data indicating a region of a cell nucleus in the analysis target image by the deep learning algorithm , wherein: the deep learning algorithm is generated from training data including pairs of first training data and second training data , the first training data includes a color density value data of each pixel generated from a bright field image of a tissue or a cell which is stained for bright field observation, the second training data includes a binarized data of each pixel generated from a fluorescence image of a same or corresponding region of the tissue or the cell whose nucleus is stained for florescence observation, wherein the binarized data indicates whether each pixel is a region of a cell nucleus, and the deep learning algorithm learns a relationship between the first training data , as an input layer of the neural network structure , and the second training data , as an output layer of the neural network structure . the deep learning algorithm is generated from training data including pairs of first training data and second training data , the first training data includes a color density value data of each pixel generated from a bright field image of a tissue or a cell which is stained for bright field observation, the second training data includes a binarized data of each pixel generated from a fluorescence image of a same or corresponding region of the tissue or the cell whose nucleus is stained for florescence observation, wherein the binarized data indicates whether each pixel is a region of a cell nucleus, and the deep learning algorithm learns a relationship between the first training data , as an input layer of the neural network structure , and the second training data , as an output layer of the neural network structure . <EOS>