Breast cancer remains a leading cause of cancer death that affects women worldwide, and the burden is felt most acutely in low resource settings where mammography access is scarce, and radiologist coverage is thin. Transfer learning-based deep learning models were evaluated for practical utility in breast cancer screening where imaging resources are restricted. The CBIS-DDSM dataset (Kaggle JPG version) was used for this study, with pathology labels mapped into a binary benign-versus-malignant classification scheme. The dataset consisted of 3,568 full mammograms and 3,461 ROI images from over 1,500 patients after quality control. 5-fold StratifiedGroupKFold cross-validation was used to prevent patient-level data leakage, which causes performance inflation in published studies, and ensured that no patient data combined training and validation datasets. The research evaluated two types of input data which included cropped region-of-interest lesion patches and complete mammogram images. InceptionV3 achieved the highest performance among ROI models by reaching ROC-AUC 0.821 and PR-AUC 0.775 and sensitivity 0.785. The study used full mammograms to evaluate ResNet50 performance which achieved ROC-AUC 0.838 and sensitivity 0.790 results. The study shows that transfer learning acts as a strong base for AI-assisted mammography triage systems in low-resource environments when patient level assessment continues throughout system development.
Zinat Abdulkadiri, Muhammad A. Suleiman, Joshua Abah· FUDMA Journal of Sciences· 0 citations
Autonomous driving has progressed from rule-based subsystems and modular perception, prediction, and planning stacks toward unified data-driven architectures, and multimodal large language models (MLLMs) are increasingly proposed as the cognitive substrate of the next generation of highly automated road vehicles. This 2026 survey synthesises 39 primary sources selected from an initial corpus of 274 candidate records screened over 2020-2026, organises the field around a five-role pipeline taxonomy (perception, prediction, planning, control, and human-machine interaction), and compares six representative driving MLLMs (DriveGPT-4, LMDrive, Senna, DriveLM, GPT-4V-AD, and Cosmos-1) on accuracy, latency, and parameter footprint. A benchmark coverage matrix over LingoQA, BDD-X, DriveLM, nuScenes-QA, AutoHallu, and CODA-LM exposes evaluation gaps in prediction and planning. Model behaviour is translated into safety-assurance terms by mapping four MLLM failure-mode families to the functional-safety standard ISO 26262, the Safety of the Intended Functionality standard ISO 21448 (SOTIF), and the autonomous-systems safety-case standard UL 4600. A three-tier vehicle, edge, and cloud deployment topology is described together with the digital-twin and over-the-air update infrastructure that surrounds it. The strongest empirical finding is that Cosmos-1 delivers the best accuracy among models with sub-150 ms latency (76.6 percent mean reasoning accuracy at 480 ms), leaving verifiable safety certification as the single most important open problem for closed-loop deployment. The survey closes with a six-item research agenda spanning sub-100 ms real-time inference, out-of-distribution generalisation, multi-agent intent reasoning, verifiable safety certification, long-tail corner-case coverage, and closed-loop sim-to-real transfer. The article is intended as a reference for automotive system architects, safety engineers, regulators, and machine-learning researchers preparing the next generation of automated driving systems.
A. O. Ogar, Joshua Abah, Ali Muhammad et al.· Journal of Science Research...· 0 citations
A unified four-quadrant taxonomy of efficiency strategies is proposed, an integrated future-research agenda built on three converging innovations: adaptive cross-modal attention re-weighting, knowledge-injection pathways, and sparse domain-conditioned neuron gating are outlined, and the cloud-aware evaluation framework that would validate them are outlined.
Olom Ogar Austin, Joshua Abah, Ali Muhammad et al.· International journal of com...· 0 citations
This survey provides a comprehensive treatment of the field across five interconnected dimensions, proposing a unified five-class taxonomy that organizes hallucinations by their failure mode: object, attribute, relational, factual, factual, and reasoning.
A. O. Ogar, Joshua Abah, M. Suleiman et al.· 0 citations
