Comparing the performance of different optimizers in convolutional neural networks for leukocyte classification

Abstract

This paper investigates the effectiveness of different optimizers for leukocyte classification in convolutional neural networks. We compare the performance of optimization functions supported by TensorFlow. Our experiment shows that “Adam” optimizer achieves the highest accuracy of 0.9844, followed by “RMS Prop” with 0.9776. The lowest accuracy of 0.4158 was achieved by “Gradient Descent”. Our study demonstrates the importance of selecting optimal optimizer for best performance in leukocyte classification tasks using CNNs on blood images.