Diagnosis processing device, diagnosis processing system, diagnosis processing method, diagnosis processing program and computer-readable recording medium, and classification processing device

A diagnosis processing method for diagnosing a target for abnormality by use of a neural network, the diagnosis processing method comprising:

• a learning pattern creating step of creating a learning pattern by (i) digitalizing a learning image into digital data, the learning image being an image in which abnormality information indicating a substantive feature of abnormality of the target is pre-known, and (ii) without extracting data of a possible abnormal site from the target, sampling data from the digital data of the learning image by use of a predetermined sampling method, the learning pattern indicating a data sequence of a sample data row of the data thus sampled;
• a learning processing step of causing a neural network to learn, by using two or more learning patterns created as such in the learning pattern creating step;
• a diagnostic pattern creating step of creating a diagnostic pattern by (iii) digitalizing a diagnostic image into digital data, the diagnostic image being an image in which abnormality information is unknown, and (iv) without extracting data of a possible abnormal site from the target, sampling data from the digital data of the diagnostic image by use of the predetermined sampling method, the diagnostic pattern indicating a data sequence of a sample data row of the data thus sampled;
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• a determining step of determining a substantive feature of abnormality of the target indicated in the abnormality information in the diagnostic image, based on an output value outputted, in response to an input of the diagnostic pattern, from a learned neural network which is the neural network subjected to the learning in the learning processing step.

A classification processing device for classifying images to two or more groups in accordance with their patterns by use of a neural network, the classification processing device comprising:

• a learning pattern creating section for creating a learning pattern by (i) digitalizing a learning image into digital data, the learning image being an image in which pattern information indicating a substantive feature of a pattern of the image is pre-known, and (ii) without extracting data of a possible site having the pattern from the image, sampling data from the digital data of the learning image by use of a predetermined sampling method, the learning pattern indicating a data sequence of a sample data row of the data thus sampled;
• a learning processing section for causing the neural network to learn, by use of two or more learning patterns created as above by the learning pattern creating section;
• a classification pattern creating section for creating a classification pattern by (iii) digitalizing a classification image into digital data, the classification image being an image in which pattern information is unknown, and (iv) without extracting data of a possible site having the pattern from the image, sampling data from the digital data of the classification image by use of the predetermined sampling method, the classification pattern indicating a data sequence of a sample data row of the data thus sampled;
• a determining section for determining a substantive feature of the pattern indicated in the pattern information in the classification image, based on an output value outputted, in response to an input of the classification pattern, from a learned neural network which is the neural network subjected to the learning;
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• a classifying section for classifying the classification image to any of the two or more groups, based on a result of determining by the determining section.