Transforming Healthcare: Raabin White Blood Cell Classification with Deep Vision Transformer.

In the realm of computer vision, the application of deep learning techniques has brought about significant transformations, particularly in the domain of image classification. This study delves into an in-depth exploration of the Raabin White Blood Cell (WBC) dataset, leveraging the power of the Deep Vision Transformer (ViT), an advanced deep learning model, to advance the classification of white blood cells. Although the Raabin WBC dataset may be relatively lesser-known, its inherent value lies in its specialized focus on white blood cell images. To harness the potential of this dataset, we meticulously curated it for compatibility with DeepViT, followed by rigorous training and fine-tuning. Our efforts yielded exceptional results, with an impressive accuracy rate of 97%, thus surpassing previous benchmarks. This remarkable performance underscores the dataset’s significance in the context of medical image analysis, highlighting its potential for various applications, particularly in the field of medical research and diagnosis. Furthermore, this study serves as a compelling testament to the adaptability and efficacy of DeepViT when applied to specialized datasets. These groundbreaking results accentuate the importance of exploring lesser-known datasets in conjunction with advanced deep learning models, as they hold the promise of delivering exceptional performance in specific domains. This work paves the way for further research and applications in medical image analysis, setting a benchmark for future white blood cell classification studies.