Acute Lymphoblastic Leukemia (ALL) is a rapidly progressing blood cancer requiring timely and accurate diagnosis for effective treatment. Automated image-based diagnostic models offer a potential solution but often lack robustness in clinical applications. This study aims to develop a robust, automated classification model for ALL subtypes using peripheral blood smear images, employing advanced feature extraction through the Swin Transformer framework. The proposed model utilizes the Swin Transformer architecture combined with Momentum Contrast (MoCo) for contrastive learning. Swin Transformer’s patch-based embedding and multi-level attention capture local and global cellular features, enhancing feature discrimination across ALL subtypes. MoCo further improves feature extraction by generating distinct embeddings, reducing overlap between cell types. The model achieved an overall classification accuracy of 92.5%, with a precision of 90.3%, recall of 91.1%, and F1-score of 90.7% across four classes (Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B). Class-specific performance metrics indicate that Malignant Pre-B achieved the highest F1-score of 92.4%. The MoCo framework demonstrated a contrastive loss reduction from 0.5 to 0.097 for benign cells, significantly enhancing feature discrimination. Additionally, an ablation study showed that omitting the dynamic queue reduced accuracy by 5%, highlighting its importance for robust feature learning. This approach can be extended to other hematologic malignancies, with potential enhancements using more extensive datasets and integration with real-time diagnostic workflows in clinical settings to support precision medicine.

Alzheimer’s disease (AD) staging via routine MRI remains challenging due to subtle morphology, class imbalance, and variability in clinical phrasing. This work presents NeuroInfinity, a lightweight prompt-driven vision–language framework that reformulates AD staging as image–text alignment on 2D T1-weighted MRI slices via a frozen CLIP ViT-B/32 encoder with minimal trainable prompt adapters. In the primary cohort, NeuroInfinity attained 96.1% accuracy, 94.1% macro-F1, and 0.983 AUC, with strong calibration (balanced accuracy of 94.2%, MCC of 0.91, ECE of 3.4%, Brier of 0.049). A targeted prompt-diversification strategy notably improves the underrepresented moderate impairment class (MoI F1 91.0%), and the model exhibits zero-shot robustness to unseen clinical phrasings (94.8% accuracy, 92.7% F1). External validation on four independent public cohorts (ImagesOASIS, ADNI, and MIRIAD) demonstrated consistent generalizability (accuracy of 94.4–95.3%) with stable calibration profiles. Designed for practical use, the framework runs at ~14 ms per slice on a mid-tier GPU and maintains linear throughput scaling. These results indicate that prompt-guided image–text alignment can deliver accurate, calibrated, and interpretable AD staging under realistic data constraints while remaining computationally efficient and extensible to prospective, multisite evaluation.

Artificial Neural Networks (ANN) has received a great attention from researchers in previous decade to predict different aspect of engineering problems. The aim of this research is to present an implementation of ANN to predict the Chloride penetration of self-consolidating concrete (SCC), containing various amounts of cement replacement minerals including fly ash, silica fume, and slag. The ability of concrete to resist chloride penetration is measured using Rapid Chloride Penetration (RCP) test through an experimental program. One- and two-layer ANN models were developed by controlling the critical parameters affecting chloride penetration to predict the results of RCP test. The ANN models were developed using various parameters including ratio of water-to-binder (w/b), course aggregate, fine aggregate, fly ash, and silica fume. It was shown that the prediction accuracy of ANN models was sensitive to combinations of learning rate and momentum. Data used to train and test the ANN were obtained through an experimental program conducted by the authors.