Advancing histopathology in Health 4.0: Enhanced cell nuclei detection using deep learning and analytic classifiers

This study contributes to the Health 4.0 paradigm by enhancing the precision of cell nuclei detection in histopathological images, a critical step in digital pathology. The presented approach is characterized by the combination of deep learning with traditional analytic classifiers. Traditional methods in histopathology rely heavily on manual inspection by expert histopathologists. While deep learning has revolutionized this process by offering rapid and accurate detections, its black-box nature often results in a lack of interpretability. This can be a significant hindrance in clinical settings where understanding the rationale behind predictions is crucial for decision-making and quality assurance. Our research addresses this gap by employing the YOLOv5 framework for initial nuclei detection, followed by an analysis phase where poorly performing cases are isolated and retrained to enhance model robustness. Furthermore, we introduce a logistic regression classifier that uses a combination of color and textural features to discriminate between satisfactorily and unsatisfactorily analyzed images. This dual approach not only improves detection accuracy but also provides insights into model performance variations, fostering a layer of interpretability absent in most deep learning applications. By integrating these advanced analytical techniques, our work aligns with the Health 4.0 initiative's goals of leveraging digital innovations to elevate healthcare quality. This study paves the way for more transparent, efficient, and reliable digital pathology practices, underscoring the potential of smart technologies in enhancing diagnostic processes within the Health 4.0 framework.