Data

The data for the project "Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks.""1 original dataset" folder, consisting of original microscopic images containing artifacts (class 0) and O.viverrini eggs (class 1) in preparation for the training and validation step."2 training dataset" folder, consisting of microscopic images containing artifacts (class 0) and O.viverrini eggs (class 1), was augmented using image rotation, filtering, adding noise, and sharpening techniques. This augmentation increased the image dataset from 1 time (100 images/class) to 36 times (3600 images/class) in preparation for the training and validation step."3 testing dataset" folder, consisting of 148 microscopic images for the testing process."4 result_heatmap" folder; our patch search algorithm detects eggs and generates a heat mapping image."5 result_detection" folder; an object detection method was proposed using a patch search algorithm to detect the O.viverrini egg and its location.</p

Codes

The code for the project "Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks.""ImageAugmentation.ipynb" file is a Python code for image augmentation in our work."meinlabproj_02_modeltrainingvalidation_rev.ipynb" file is a Python code for the model construction and validation and then export to the h5 file."ObjectDetection_Rev.ipynb" is a Python code for importing testing images and h5 files for patch searching and creating a bounding box around the O. viverrini egg.</p