Aims Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagnosis of acute leukaemia using peripheral blood (PB) images.
Methods A set of 442 smears was analysed from 206 patients. It was split into a training set with 75% of these smears and a testing set with the remaining 25%. Colour clustering and mathematical morphology were used to segment cell images, which allowed the extraction of 2,867 geometric, colour and texture features. Several classification techniques were studied to obtain the most accurate classification method. Afterwards, the classifier was assessed with the images of the testing set. The final strategy was to predict the patient’s diagnosis using the PB smear, and the final assessment was done with the cell images of the smears of the testing set.
Results The highest classification accuracy was achieved with the selection of 700 features with linear discriminant analysis. The overall classification accuracy for the six groups of cell types was 85.8%, while the overall classification accuracy for individual smears was 94% as compared with the true confirmed diagnosis.
Conclusions The proposed method achieves a high diagnostic precision in the recognition of different types of blast cells among other mononuclear cells circulating in blood. It is the first encouraging step towards the idea of being a diagnostic support tool in the future.
- image analysis
- automatic classification
- morphological analysis
- peripheral blood
Statistics from Altmetric.com
Handling editor Prof Mary Frances McMullin.
Contributors All the authors have fullfilled the criteria of authorship and have done substantial contributions to the study and the interpretation of data. They have revised the work, as well as the current revision of the manuscript.
Funding The study was funded by the Directory of Science, Technology and Innovation of the Ministry of Economy and Competitiveness of Spain (grant number: DPI2015-64493-R)
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.