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White blood cell detection plays an integral role in diagnosing pathologies such as leukemia and gestational diabetes. Despite this, conventional image-based white blood cell classification methodologies encounter obstacles including inaccurate cell segmentation and labor-intensive artificial feature extraction. Contrarily, Convolutional Neural Networks (CNNs) have the capacity to learn features autonomously from raw images, thereby offering a novel and effective solution for blood cell detection. Notwithstanding, the features ascertained by a solitary CNN tend to be unidirectional. Conversely, ensemble learning combines results from numerous networks, thus ensuring an adequate acquisition of feature information and subsequently enhancing the model's overall efficacy. Consequently, this study introduces a method for white blood cell classification underpinned by ensemble CNNs. Initially, three high-performing CNNs possessing disparate structures, namely VGG16, ResNet50, and Inception V3, are enlisted as base learners to augment the diversity of base learners. Subsequently, the Gompertz function is employed to strategize the ensemble learning combination strategy, taking into consideration the prediction confidence and fuzzy level of each base learner. Ultimately, the ensemble CNN model is developed, incorporating learning outcomes from several singular models and utilizing diversified information to achieve white blood cell classification. Empirical results indicate that the ensemble learning technique advanced in this study enables accurate and reliable white blood cell classification, demonstrating potential clinical value. [ABSTRACT FROM AUTHOR]