A medical image processing apparatus adapted to improve image quality characterized by comprising: a memory configured to store a plurality of trained neural networks (135, 135') corresponding to a plurality of anatomical structures respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding anatomical structures; and processing circuitry configured to: receive first data corresponding to an anatomical structure, the first data being computer tomography "CT" data; select a neural network from the plurality of trained neural networks corresponding to the anatomical structure of the first data; and process the first data into second data using the selected neural network, wherein the first data is input to the input layer and the second data is output from the output layer, the memory being further configured to store a plurality of neural networks corresponding to a plurality of imaging conditions, respectively, the neural networks corresponding to the respective imaging conditions each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired under imaging conditions equal to or similar to the corresponding imaging condition; and the processing circuitry being further configured to process the second data into third data using, among the neural networks corresponding to the respective imaging conditions, the neural network corresponding to the imaging condition for the second data, wherein the second data is input to the input layer and the third data is output from the output layer. a memory configured to store a plurality of trained neural networks (135, 135') corresponding to a plurality of anatomical structures respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding anatomical structures; and processing circuitry configured to: receive first data corresponding to an anatomical structure, the first data being computer tomography "CT" data; select a neural network from the plurality of trained neural networks corresponding to the anatomical structure of the first data; and process the first data into second data using the selected neural network, wherein the first data is input to the input layer and the second data is output from the output layer, the memory being further configured to store a plurality of neural networks corresponding to a plurality of imaging conditions, respectively, the neural networks corresponding to the respective imaging conditions each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired under imaging conditions equal to or similar to the corresponding imaging condition; and the processing circuitry being further configured to process the second data into third data using, among the neural networks corresponding to the respective imaging conditions, the neural network corresponding to the imaging condition for the second data, wherein the second data is input to the input layer and the third data is output from the output layer. receive first data corresponding to an anatomical structure, the first data being computer tomography "CT" data; select a neural network from the plurality of trained neural networks corresponding to the anatomical structure of the first data; and process the first data into second data using the selected neural network, wherein the first data is input to the input layer and the second data is output from the output layer, the memory being further configured to store a plurality of neural networks corresponding to a plurality of imaging conditions, respectively, the neural networks corresponding to the respective imaging conditions each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired under imaging conditions equal to or similar to the corresponding imaging condition; and the processing circuitry being further configured to process the second data into third data using, among the neural networks corresponding to the respective imaging conditions, the neural network corresponding to the imaging condition for the second data, wherein the second data is input to the input layer and the third data is output from the output layer.