Campbellsville University

The convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies. Overall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. Addressing these limitations will be critical for translating QML from experimental feasibility to routine medical practice (Marengo and Santamato, 2025; Rawas and AlSaeed, 2026).

Abstract Accurate white blood cell classification is an important component of hematological image analysis, yet robust automation remains challenging because microscopic smear images often exhibit substantial morphological similarity across subtypes, staining variation, acquisition heterogeneity, and sensitivity to surrounding background content. Existing single-backbone approaches may not fully capture the complementary local texture, cellular morphology, and global contextual information required for reliable recognition across heterogeneous white blood cell images, while the effects of preprocessing choices such as cropping and image standardization are often insufficiently examined. To address these issues, this study proposes a three-branch hybrid fusion architecture for five-class white blood cell classification on a combined PBC-Normal and Raabin-WBC dataset comprising 27,606 images, including 3337 basophils, 6158 eosinophils, 4685 lymphocytes, 4,108 monocytes, and 9318 neutrophils. The framework integrates MobileNetV3, DenseNet121, and pretrained DINOv2 feature branches to capture lightweight convolutional cues, densely reused local representations, and transformer-based global semantic features, respectively. Branch-wise 512-dimensional embeddings are combined through feature stacking, layer normalization, multi-head attention, and a residual-style classification head for final five-class prediction. Performance was examined under four image-preparation settings, namely not cropped and unprocessed, not cropped and processed, cropped and unprocessed, and cropped and processed, together with dataset-specific analyses on PBC-Normal and Raabin-WBC. Across these comparative experiments, the proposed model achieved the strongest overall performance, with particularly high results on the PBC-Normal dataset and consistently favorable behavior across the combined-dataset evaluation settings, while processed inputs generally improved convergence and classification quality relative to unprocessed counterparts. These findings indicate that attention-guided CNN-transformer fusion, combined with controlled preprocessing evaluation, provides an effective and robust framework for automated five-class white blood cell classification across heterogeneous microscopic images.

The rapid expansion of Industrial Internet of Things (IIoT) infrastructures has increased the need for intrusion detection systems that remain reliable under severe class imbalance, heterogeneous protocol behavior, and rare but high-impact attacks. Existing IIoT detectors often achieve high aggregate accuracy but remain sensitive to manual hyperparameter tuning and underperform on minority attack classes. This study proposes an Attention-Guided Quantum-Inspired Extreme Learning Machine with Grey Wolf Optimization for rare-attack detection in IIoT networks. The framework represents each Edge-IIoT flow as a fixed-order 48-feature vector and uses a multi-head self-attention encoder with learned feature-index embedding to model cross-feature structural dependencies. A Quantum-Inspired Extreme Learning Machine provides closed-form classification through quantum-rotation-based hidden-layer initialization, while Grey Wolf Optimization selects seven key hyperparameters. Focal Loss with inverse-frequency class weighting is used to improve learning from underrepresented classes. The model was evaluated on the Edge-IIoT dataset containing 1,909,671 records, 15 traffic classes, and 48 features under binary and multi-class settings. In binary Normal-versus-Attack classification, the proposed model achieved 99.54

Background Mental health disorders affect approximately one in four people globally, yet treatment-seeking rates remain persistently low, particularly in high-stress professional environments such as the technology industry. Understanding the factors that predict whether an individual will seek mental health treatment is critical for designing effective workplace interventions. Methods This study applies five supervised machine learning classification algorithms — Random Forest, Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes, and eXtreme Gradient Boosting (XGBoost) — to predict treatment-seeking behaviour using the 2016 Open Sourcing Mental Illness (OSMI) survey dataset. The dataset includes responses from 1,434 technology industry workers across multiple countries. After preprocessing, including removal of high-missingness features and standardisation of categorical fields, a refined dataset of 960 entries was used for model training and evaluation. Feature correlation analysis was conducted to identify the strongest predictors of treatment-seeking behaviour. Results XGBoost achieved the highest classification accuracy of 88.7%, outperforming Random Forest (87.1%), Logistic Regression (87.1%), Gaussian Naive Bayes (86.6%), and SVM (85.6%). The most significant predictors of treatment-seeking behaviour were a prior diagnosis of a mental disorder and a family history of mental illness. A marked gender disparity was observed: male-identifying respondents reported substantially lower treatment-seeking rates despite similar rates of self-reported mental disorders. Conclusions Machine learning approaches, particularly XGBoost, demonstrate strong predictive capability for mental health treatment-seeking behaviour in technology industry workers. The identified gender disparity suggests a need for targeted workplace mental health interventions directed at male-identifying employees. These findings contribute to the growing evidence base for data-driven approaches to mental health decision support.