On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition
<|reference_start|>On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition: Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine learning (ML) analysis among microscopy techniques. The digital format of AFM images allows for direct utilization in ML algorithms without the need for additional processing. Additionally, AFM enables the simultaneous imaging of distributions of over a dozen different physicochemical properties of sample surfaces, a process known as multidimensional imaging. While this wealth of information can be challenging to analyze using traditional methods, ML provides a seamless approach to this task. However, the relatively slow speed of AFM imaging poses a challenge in applying deep learning methods broadly used in image recognition. This Prospective is focused on ML recognition/classification when using a relatively small number of AFM images, 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.<|reference_end|>
@article{sokolov2024on,
    title={On machine learning analysis of atomic force microscopy images for image
  classification, sample surface recognition},
    author={Igor Sokolov},
    journal={arXiv preprint arXiv:2403.16230},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16230},
    primaryClass={physics.bio-ph cs.AI physics.ins-det physics.med-ph}
}