Abstract
White blood cell (WBC) classification is a crucial step in assessing a patient's health and validating medical treatment in the medical domain. Hence, efficient computer vision solutions to the classification of WBC will be an effective aid to medical practitioners. Computer-aided diagnosis (CAD) reduces manual intervention, avoids errors, speeds up medical analysis, and provides accurate medical reports. Though a lot of research has been taken up to develop deep learning models for efficient classification of WBCs, there is still scope for improvement to support the data insufficiency issue in medical data sets. Data augmentation and normalization techniques increase the quantity of data but don't enhance the quality of the data. Hence, deep learning models though performing well can still be made efficient and effective when quality data is fused along with the available image dataset. This paper aims to utilize domain knowledge and image data to improve the classification performance of pre-trained models namely Inception V3, DenseNet 121, ResNet 50, MobileNet V2, and VGG 16. The models performance, with and without domain knowledge infused, is analyzed on the BCCD and LISC datasets. On the BCCD dataset, the average accuracies increased from 82.7%, 98.8%, 98.38%, 98.56%, and 98.5%-99.38%, 99.05%, 99.05%, 98.67%, and 98.75% for Inception V3, DenseNet 121, ResNet 50, MobileNet V2, and VGG 16, respectively. Similarly, on the LISC dataset, the accuracies improved from 86.76%, 92.2%, 91.76%, 92.8%, and 94.4%-92.05%, 95.88%, 95.58%, 95.2%, and 95.2%, respectively.