Method for detecting lesion and computer-readable recording medium storing lesion detection program

A non-transitory computer-readable recording medium storing a lesion detection program for causing a computer to execute a process comprising:

• a training process that includes:
• calculating a first image feature, based on a plurality of first tomographic image groups obtained by imaging an inside of each of a plurality of first human bodies, for each of the first tomographic image groups;
• classifying tomographic images included in the plurality of first tomographic image groups into a plurality of second tomographic image groups, according to a range of the first image feature;
• and
• generating a plurality of first lesion identification models configured to identify whether or not unit image regions included in the tomographic images to be identified are regions of a particular lesion, for each of the unit image regions, each through machine learning that uses different ones of the plurality of second tomographic image groups from each other, as training data;
• and
• a lesion detection process that includes:
• calculating a second image feature of a same type as the first image feature, based on a plurality of first tomographic images obtained by imaging the inside of a second human body;
• acquiring, from each of the plurality of first lesion identification models, probabilities of being the regions of the particular lesion, for each of the unit image regions included in the plurality of first tomographic images, by inputting the plurality of first tomographic images to each of the plurality of first lesion identification models;
• integrating, for each of the unit image regions included in the plurality of first tomographic images, the probabilities acquired from each of the plurality of first lesion identification models, based on the second image feature, to calculate an integrated value;
• and
• detecting the regions of the particular lesion from each of the plurality of first tomographic images, based on the integrated value.