2024 27th International Conference on Computer and Information Technology (ICCIT)
2024ConferenceResidual Block-Driven CNN for Accurate White Blood Cell Image Analysis and Classification
Our findings show that using residual blocks is a very effective approach for detecting and classifying white blood cells (WBCs). As residual block offers lossless data restoration, We built a custom CNN model with residual blocks and achieved an accuracy of around 97.65%. We also tested our dataset (Raabin-WBC [4]) on other models that use depthwise separable convolutions, like MobileNet, InceptionNet, and XceptionNet. After fine-tuning, these models achieved accuracies of 97.85%, 98.40%, and 98%, respectively.
Convolutional neural networksFeature extractionMedical diagnosisOrganismsProtectionResidual neural networksWhite blood cells
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