On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition
Igor Sokolov
Abstract
small database. We discuss ML methods other than popular deep-learning neural networks. The described approach has already been successfully used to analyze and classify the surfaces of biological cells. It can be applied to recognize medical images, specific material processing, in forensic studies, even to identify the authenticity of arts. A general template for ML analysis specific to AFM is suggested, with a specific example of the identification of cell phenotype. Special attention is given to the analysis of the statistical significance of the obtained results, an important feature that is often overlooked in papers dealing with machine learning. A simple method for finding statistical significance is also described.
Topics & Concepts
Atomic force microscopyArtificial intelligenceSample (material)MicroscopyPattern recognition (psychology)Surface (topology)Materials scienceComputer scienceComputer visionNanotechnologyChemistryOpticsPhysicsMathematicsChromatographyGeometryForce Microscopy Techniques and ApplicationsAdvanced Surface Polishing TechniquesNanofabrication and Lithography Techniques