Efficient white blood cell identification with hybrid inception-xception network

White blood cells (WBCs) are crucial microscopic defenders of the human immune system in combating transmittable conditions triggered by germs, infections, and various other human pathogens. Timely and appropriate WBC detection and classification are decisive for comprehending the immune system’s standing and its feedback to various pathologies, assisting in diagnosing and monitoring illness. Nevertheless, the manual classification of WBCs is strenuous, extensive, and prone to errors, while automated approaches can be cost-prohibitive. Within artificial intelligence, deep learning (DL) approaches have become an appealing option for automating WBC recognition. The existing DL techniques for WBC classification face several limitations and computational difficulties, such as overfitting, limited scalability, and design complexity, often battling with function variety in WBC images and requiring considerable computational resources. This research study recommends an ingenious hybrid inception-xception Convolutional Semantic network (CNN) designed to deal with constraints in existing DL versions. The proposed network incorporates inception and depth-separable convolution layers to successfully catch attributes across many ranges, therefore minimizing concerns related to model complexity and overfitting. In contrast to traditional CNN designs, the proposed network lessens the layers made use of and increases their function removal capacities, hence enhancing the performance of WBC classification, which needs a wide variety of attribute abilities. Furthermore, the proposed model was trained, validated and tested on three popular and widely recognized datasets, namely, Leukocyte Images for Segmentation and Classification (LISC), Blood Cell Count and Detection (BCCD), and Microscopic PBS (PBS-HCB), where it demonstrates the generalization and robustness and superiority of our proposed model. The model depicted an outstanding average accuracy rate of 99.25%, 99.65%, and 98.6% on a five-fold cross-validation test for the respective datasets, surpassing existing models as detailed. The model’s robustness and superior performance, validated across diverse datasets, underscore its potential as an advanced tool for accurate and efficient WBC classification in medical diagnostics.