Transformer models have demonstrated remarkable success in various fields, but face notable challenges in blood cell classification. These challenges arise primarily from two factors: (1) high computational demands due to the substantial number of parameters, and (2) insufficient labeled data in blood cell datasets, leading to overfitting risks. To address these issues, we introduce BccT, an innovative Transformer-based model specifically designed to optimize the classification of blood cell images. Central to our approach is the Token Fusion module, which intelligently merges similar tokens, thereby enhancing computational efficiency and reducing training overhead without compromising performance. This novel mechanism contrasts with traditional Transformer methods that process each token independently, resulting in significant redundancy. Additionally, we propose the Fixed-Random Classifier to mitigate overfitting risks, particularly in limited-data environments. Unlike conventional classifiers that necessitate constant parameter updates across all layers, our Fixed-Random Classifier comprises only three layers with predominantly static, randomly-initialized parameters, thus reducing dependency on extensive labeled data. Extensive experiments validate the effectiveness of our model. Using ViT_base as the backbone, our model achieves similar accuracy in half the training time compared to the ViT_base model. In experiments on four public blood cell datasets, our model improves accuracy by approximately 4.5% compared to other state-of-the-art methods.

Since the COVID-19 pandemic outbreak, over 760 million confirmed cases and over 6.8 million deaths have been reported globally, according to the World Health Organization. While the SARS-CoV-2 virus carried by COVID-19 patients can be identified though the reverse transcription–polymerase chain reaction (RT-PCR) test with high accuracy, clinical misdiagnosis between COVID-19 and pneumonia patients remains a challenge. Therefore, we developed a novel CovC-ReDRNet model to distinguish COVID-19 patients from pneumonia patients as well as normal cases. ResNet-18 was introduced as the backbone model and tailored for the feature representation afterward. In our feature-based randomized neural network (RNN) framework, the feature representation automatically pairs with the deep random vector function link network (dRVFL) as the optimal classifier, producing a CovC-ReDRNet model for the classification task. Results based on five-fold cross-validation reveal that our method achieved 94.94%, 97.01%, 97.56%, 96.81%, and 95.84% MA sensitivity, MA specificity, MA accuracy, MA precision, and MA F1-score, respectively. Ablation studies evidence the superiority of ResNet-18 over different backbone networks, RNNs over traditional classifiers, and deep RNNs over shallow RNNs. Moreover, our proposed model achieved a better MA accuracy than the state-of-the-art (SOTA) methods, the highest score of which was 95.57%. To conclude, our CovC-ReDRNet model could be perceived as an advanced computer-aided diagnostic model with high speed and high accuracy for classifying and predicting COVID-19 diseases.