White Blood Cell Classification Using Convolutional Neural Network

White blood cells (WBCs) are a key element of the immune system and demonstrate resistance to variety illnesses, quantitative qualitative examination various kinds white is critical. Counting categorizing types WBCs can help doctors detect treat different illnesses. As result, one most important steps in analyzing testing samples counting WBCs. The main purpose this study provide CNN based model for processing with aim classifying type these cells. Kaggle images were used article, we built CNN-based cell assessed model's performance using several optimizers. We have seen that RMSprop optimizer shows best result our proposed model. compared four pre-trained models such as MobileNetV2, DenseNet121, InceptionV3 ResNet50 Compared models, other related studies, lowest number trainable parameters training time great results 99.5% accuracy, 99% recall, precision, F1 score.