Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues

2025/02/15 by Muhammad Ali, Viviana Benfante, Ghazal Basirinia +9 · 1 voice · 64 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Artificial intelligence #Artificial neural network #Automatic image annotation #Cell Image Analysis Techniques #Computer science #Deep learning #Digital Imaging for Blood Diseases #Feature extraction #Image (mathematics) #Image analysis #Image processing #Image segmentation #Machine learning #Pattern recognition (psychology) #Segmentation

paper · doi:10.3390/jimaging11020059

published in Journal of Imaging 11(2), 59 (Multidisciplinary Digital Publishing Institute)

openalex publication_date 2025/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.

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Discussions

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