A computer-implemented image segmentation method, which is performed in a computing device having one or more processors and memory for storing one or more programs executed by the one or more processors, comprising the steps of: (a) receiving an input image (400, 500, 600); (b) generating a first-generation image segment set by dividing the input image (400, 500, 600) in an overlapped manner, wherein a window of a preset size is arranged on an input image and while sequentially moving the window in the input image by a predetermined distance in an X-axis direction and an Y-axis direction, each region included in the window is extracted as an image segment of the input image, wherein a moving distance of the window in the X-axis direction is set smaller than a length of the image in the X-axis direction, and a moving distance of the window in the Y-axis direction is set smaller than a length of the image in the Y-axis direction; and (c) generating a second or higher-generation image segment set from the first-generation image segment set, wherein a subsequent-generation image segment set is generated by dividing in an overlapped manner at least one of a plurality of image segments comprised in the previous-generation image segment set; (d) reducing the input image (400, 500, 600) and some or all of the image segments comprised in each of the first to nth-, where n is an integer greater than or equal to 2, generation image segment sets to a preset size; and (e) training a deep learning-based image classifier using the reduced images, wherein each of a plurality of image segments comprised in a jth-, where j is an integer greater than or equal to 1, generation image segment set includes a region that overlaps one or more other image segments included in the jth-generation image segment set. (a) receiving an input image (400, 500, 600); (b) generating a first-generation image segment set by dividing the input image (400, 500, 600) in an overlapped manner, wherein a window of a preset size is arranged on an input image and while sequentially moving the window in the input image by a predetermined distance in an X-axis direction and an Y-axis direction, each region included in the window is extracted as an image segment of the input image, wherein a moving distance of the window in the X-axis direction is set smaller than a length of the image in the X-axis direction, and a moving distance of the window in the Y-axis direction is set smaller than a length of the image in the Y-axis direction; and (c) generating a second or higher-generation image segment set from the first-generation image segment set, wherein a subsequent-generation image segment set is generated by dividing in an overlapped manner at least one of a plurality of image segments comprised in the previous-generation image segment set; (d) reducing the input image (400, 500, 600) and some or all of the image segments comprised in each of the first to nth-, where n is an integer greater than or equal to 2, generation image segment sets to a preset size; and (e) training a deep learning-based image classifier using the reduced images, wherein each of a plurality of image segments comprised in a jth-, where j is an integer greater than or equal to 1, generation image segment set includes a region that overlaps one or more other image segments included in the jth-generation image segment set.