Automated Image Analysis for Hematological Disorders

Automated image analysis in haematological disorders has emerged as a transformative approach to augment traditional microscopy by combining advanced image-processing algorithms with machine learning. Modern workflows typically begin with pre-processing steps to normalise staining variations and correct illumination artefacts, followed by image segmentation to detect individual cells. Subsequent feature extraction quantifies morphological attributes—such as cell shape, size, texture and colour—before classification models assign diagnostic labels or subtype categories. Early systems relied on handcrafted features and classical classifiers, whereas contemporary pipelines increasingly employ deep convolutional neural networks to learn hierarchical representations directly from raw pixels. Applications span anaemia screening, sickle cell disease monitoring, thalassaemia classification and beyond, with particular impact in resource-limited settings and telemedicine. Integration with laboratory information systems and high-throughput slide scanners has demonstrated potential for rapid, large-scale screening and standardisation of diagnostic practice. Looking forward, emerging trends include the fusion of image data with multi-omics profiles, real-time analysis at point of care and the development of explainable AI frameworks to bolster clinical confidence and regulatory acceptance.

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Automated Image Analysis for Hematological Disorders publication trend

The graph below shows the total number of articles in automated image analysis for hematological disorders across all publications each year (not limited to Nature Index journals).

Technical terms

Peripheral blood smear: A thin layer of blood spread on a glass slide for microscopic examination of cellular morphology.

Image segmentation: The computational process of partitioning an image into regions corresponding to individual cells or structures of interest.

Convolutional neural network: A deep learning architecture that applies convolutional filters to extract hierarchical spatial features from images.

Feature extraction: The transformation of raw image data into quantitative descriptors (e.g. shape, texture, colour) suitable for classification.

References

1. A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLOS Computational Biology (2017).
2. Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases. IEEE Access (2020).
3. Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review. Medical & Biological Engineering & Computing (2022).