Revolutionizing Blood Cell Analysis: The Emergence of EfficientNetB7-Enhanced Deep Learning Models

The rapid evolution of machine learning (ML) in medical diagnostics has prompted a surge in the development of models capable of interpreting complex biological data with unprecedented precision. In the quest to leverage computational intelligence for enhancing diagnostic accuracy, this study explores the application of deep learning (DL) in the nuanced field of hematology. This article describes a better Convolutional Neural Network (CNN) model that is based on the EfficientNetB7 architecture and has been carefully tuned to sort blood cells into different groups. Our model addresses common challenges such as data scarcity, class imbalance, and the need for computational efficiency. We adopt an innovative fine-tuning strategy that adjusts the model parameters to the intricacies of the blood cell images while simultaneously employing weighted loss functions to tackle class imbalance effectively. Through extensive experimentation and evaluation, the proposed model achieves a remarkable 99% accuracy on the test set, outperforming existing models and setting a new standard in medical image analysis. The study's results indicate that our model can significantly enhance the accuracy and speed of blood cell classification, offering substantial potential for clinical application. Future work will look into the model's integration with live diagnostic systems and expansion to multi-modal medical data analysis.