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Size of the training dataset is an important factor in the performance of amachine learning algorithms and tools used in medical image processing are notexceptions. Machine learning tools normally require a decent amount of trainingdata before they could efficiently predict a target. For image processing andcomputer vision, the number of images determines the validity and reliabilityof the training set. Medical images in some cases, suffer from poor quality andinadequate quantity required for a suitable training set. The proposedalgorithm in this research obviates the need for large or even small imagedatasets used in machine learning based image enlargement techniques byextracting the required data from a single image. The extracted data was thenintroduced to a decision tree regressor for upscaling greyscale medical imagesat different zoom levels. Results from the algorithm are relatively acceptablecompared to third-party applications and promising for future research. Thistechnique could be tailored to the requirements of other machine learning toolsand the results may be improved by further tweaking of the toolshyperparameters.

A Novel Pixel-Averaging Technique for Extracting Training Data from a Single Image, Used in ML-Based Image Enlargement