A Novel Framework for Accurate Multiclass Blood Cell Classification Using Deep Learning
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Keywords:
CNN, Blood Cells, Plasma, RBC, Deep Learning, Imbalanced DatasetAbstract
This work introduces a new approach for classification of blood cells that handles issues
imbalance dataset between different categories and the presence of several classes. The main part of the
proposed approach is ResNet-18, a deep neural network designed for both strong and efficient feature
extraction and classification. We implemented class balancing techniques to eliminate the problem of
imbalanced classes in the training data. The model was able to classify Plasma Cell, Basophil, Eosinophils,
Erythroblast, Lymphocyte, Monocyte, nRBC and Platelet among the blood cell types. The study showed
that the model achieved an accuracy of 99.12%, precision of 99.12%, recall of 99.13% and F1-score of
99.12%. A deep learning framework makes blood cell analysis dependable and easily scalable, helping
solve major issues with classifying medical images.
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