Authors: A.M. Patil, Mukesh D. Patil, Gajanan K. Birajdar

Journal: Innovation and Research in BioMedical Engineering (Elsevier)

Overview

White blood cells (WBCs) play a crucial role in diagnosing and monitoring various health conditions. Recognizing and characterizing WBCs from patient blood samples is a foundational step in detecting blood-related diseases. Traditional techniques using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their hybrid architectures have enhanced the interpretive power of medical image content, especially with the advent of big data in healthcare.

However, one of the critical challenges in WBC image classification has been the overlapping of multiple cells within an image. This overlap can obstruct accurate segmentation, leading to misclassification and hindering the reliability of the model's outputs. This study proposes a Canonical Correlation Analysis (CCA)-based deep learning approach to overcome these limitations, aiming to improve classification accuracy and speed.

Objectives

Enhance Image Classification Accuracy: Address the limitations posed by overlapping cells in blood sample images.

Reduce Classification Time: Decrease the time required to classify WBC images by improving the efficiency of model training and convergence.

Optimize Model Performance: Achieve a high success rate in WBC image classification while reducing the input image dimensions and ensuring fast network convergence.

Methodology

The authors developed a framework combining CNN and RNN architectures with Canonical Correlation Analysis (CCA) to overcome challenges associated with overlapping nuclei in WBC images. Key methodological highlights include:

Single-Cell Patch Extraction: Leveraging CNNs and RNNs for improved image feature extraction, the model isolates individual patches of cells from the image. This stage is critical for managing images where multiple WBCs overlap, allowing the model to focus on distinct, non-overlapping cell patches.

CCA for Feature Correlation: The CCA technique identifies the relationship between overlapping cells by extracting and analyzing correlated features across multiple overlapping cell nuclei. CCA aids in dimensional reduction, minimizing irrelevant information from overlapping regions and preserving meaningful data.

Hybrid CNN-RNN-CCA Model: By fusing CNN's spatial feature extraction capabilities with RNN's sequential processing, combined with CCA’s dimensional reduction and feature correlation, the model achieves a robust and efficient classification framework.

Key Findings

The experimental evaluation was conducted on a publicly available WBC image database, and the hybrid CNN-RNN-CCA model was benchmarked against other state-of-the-art classification techniques. The results revealed:

Higher Classification Accuracy: The CNN-RNN model, combined with CCA, significantly outperformed traditional blood cell classification techniques, demonstrating a more accurate identification and classification of WBCs, even in images with overlapping cells.

Reduced Classification Time: The inclusion of CCA optimized feature extraction, reducing computational complexity, compressing image dimensions, and enabling faster convergence of the network.

Effective Handling of Overlapping Cells: The CCA component effectively isolated and learned from overlapping nuclei, allowing the model to train on regions that are otherwise challenging in single-cell patch methods, enhancing its robustness.

Implications and Future Applications

This study introduces a promising approach for improving WBC classification accuracy and efficiency in medical image analysis. With the advancement of deep learning, CCA-based models can have broader applications in medical diagnostics by providing reliable and rapid classification of complex cell images. Potential future research directions include:

Application in Multi-Class Blood Cell Classification: Expanding the hybrid model to classify additional blood cell types (e.g., red blood cells, platelets) may provide comprehensive diagnostics for blood sample analysis.

Real-Time Diagnostics: Integrating this model into real-time diagnostic systems could streamline in-hospital or clinical laboratory workflows, enabling faster patient diagnosis.

Deployment in Edge Computing for Healthcare: Optimizing the model for deployment on edge devices (such as portable diagnostic equipment) could expand access to advanced diagnostics in remote or resource-limited settings.

Conclusion

The CNN-RNN-CCA model described in this study offers a robust solution to the limitations posed by overlapping cells in WBC image classification. By enhancing the accuracy and efficiency of WBC classification, this approach contributes to the growing capabilities of AI in medical diagnostics. The research highlights the potential for deep learning and CCA to reshape medical imaging analysis, ultimately supporting better patient outcomes through advanced and efficient healthcare diagnostics.