  Size of the training dataset is an important factor in the performance of a
machine learning algorithms and tools used in medical image processing are not
exceptions. Machine learning tools normally require a decent amount of training
data before they could efficiently predict a target. For image processing and
computer vision, the number of images determines the validity and reliability
of the training set. Medical images in some cases, suffer from poor quality and
inadequate quantity required for a suitable training set. The proposed
algorithm in this research obviates the need for large or even small image
datasets used in machine learning based image enlargement techniques by
extracting the required data from a single image. The extracted data was then
introduced to a decision tree regressor for upscaling greyscale medical images
at different zoom levels. Results from the algorithm are relatively acceptable
compared to third-party applications and promising for future research. This
technique could be tailored to the requirements of other machine learning tools
and the results may be improved by further tweaking of the tools
hyperparameters.
