Biomedical image interpretation is fundamental to achieving robust imaging performance and supporting numerous clinical workflows. For accurate diagnosis of blood disorders involving red blood cells, these cells must be reliably detected and classified. Manual evaluation is labor-intensive and may introduce inaccuracies. Multi-label samples containing clusters of cells remain difficult to analyze because separating individual elements—especially when cells overlap or touch—is challenging. Modern biosensing technologies now enable highly capable biomedical imaging and support multiple medical applications. In this work, we design an intelligent neural architecture able to autonomously recognize and categorize red blood cells from microscopic imagery by employing region-based convolutional neural networks (RCNN) combined with advanced biosensors. The system effectively handles issues such as cell overlap or contact and can correctly identify diverse blood cell morphologies. Data augmentation was applied as a preliminary processing step to expand the dataset and boost computational performance. To further optimize image quality and suppress noise, we applied the Radial Gradient Index filtering technique for intensity normalization. When evaluated on medical imaging datasets, our approach demonstrated superior detection accuracy and lower loss compared with established models such as ResNet and GoogleNet. The model achieved 99% accuracy during training and 91.21% accuracy on test images. Overall, our method exceeded the performance of ResNet-50 and GoogleNet by roughly 10–15%. These findings indicate that AI-driven automated RBC detection can streamline hematological analysis, decrease human error, and promote timely disease identification.
