Mehran Ali

Review Open access Aug 2026

An Explainable Machine Learning Framework for Early Detection of Brain Tumours with Statistical Analysis of Clinical and Imaging Data

Early and accurate detection of brain tumors is critical for improving patient outcomes, yet diagnostic delays and interpretability gaps limit the clinical adoption of artificial intelligence (AI) systems. This study proposes an explainable machine learning framework that integrates clinical and imaging data for early detection of brain tumors. Using a retrospective cohort of 1,248 patients, we developed and evaluated baseline statistical models, ensemble methods, and deep learning architectures, with systematic incorporation of SHAP values, Grad-CAM heatmaps, and patient-specific explanations. The hybrid model achieved an AUC-ROC of 0.97, accuracy of 93.2%, and well-calibrated predictions, outperforming imaging-only and tabular baselines. Explainability outputs demonstrated high concordance with radiologist annotations and were rated as clinically plausible. The framework maintained robust performance across tumor types, age groups, and imaging modalities, including CT-only cases. These findings support the feasibility of trustworthy AI-assisted diagnostics in neuro-oncology, particularly for resource-constrained settings. Future work should focus on prospective validation, human-factors evaluation, and integration with molecular data to further enhance clinical utility and generalizability. REFERENCES [1] World Health Organization, "Research and development landscape for childhood cancer: a 2023 perspective," World Health Organization, 2023. [2] U. Iqbal and Y. Bhutto, "Digital transformation through artificial intelligence and advance business analytic in American operational management," Journal of Theoretical and Applied Econometrics, vol. 3, no. 1, pp. 37-50, 2026. [3] Q. T. Ostrom, M. Price, C. Neff, G. Cioffi, K. A. Waite, C. Kruchko, and J. S. Barnholtz-Sloan, "CBTRUS statistical report: primary brain and other central nervous system tumours diagnosed in the United States in 2015--2019," Neuro-oncology, vol. 24, no. Supplement_5, pp. v1-v95, 2022. [4] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, et al., "A survey on deep learning in medical image analysis," Medical image analysis, vol. 42, pp. 60-88, 2017. [5] E. J. Topol, "High-performance medicine: the convergence of human and artificial intelligence," Nature medicine, vol. 25, no. 1, pp. 44-56, 2019. [6] M. A. Rahman, M. I. K. Jabed, R. K. Devnath, C. M. Mehedi, M. Begum, and T. Mahmud, "MSRFF: An Interpretable CNN--Vision Transformer Framework for Diabetic Retinopathy Detection," in 2026 2nd International Conference on Computational Intelligence Approaches and Applications (ICCIAA), 2026, pp. 1-7. [7] B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, "Machine learning for medical imaging," radiographics, vol. 37, no. 2, pp. 505-515, 2017. [8] S. M. Lundberg and S. I. Lee, "A unified approach to interpreting model predictions," Advances in neural information processing systems, vol. 30, 2017. [9] W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K. R. Müller, Eds., Explainable AI: interpreting, explaining and visualizing deep learning. Springer Nature, 2019. [10] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., "The multimodal brain tumour image segmentation benchmark (BRATS)," IEEE transactions on medical imaging, vol. 34, no. 10, pp. 1993-2024, 2014. [11] U. Iqbal, "AI-enhanced network optimization for electric vehicle charging infrastructure expansion in the United States using graph theory and demand analytics," Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 112-129, 2024. [12] M. I. K. Jabed, M. Imran, A. A. Khan, M. Mehedi, A. Islam, and R. Pervez, "Explainable Machine Learning Framework for Early Heart Disease Detection Using SMOTE and SHAP," Vascular and Endovascular Review, vol. 9, no. 1, pp. 316-324, 2026. [13] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the best machine learning algorithms for brain tumour segmentation, progression assessment, and overall survival prediction in the BRATS challenge," arXiv preprint arXiv:1811.02629, 2018. [14] R. Obuchowicz, J. Lasek, M. Wodziński, A. Piórkowski, M. Strzelecki, and K. Nurzynska, "Artificial intelligence-empowered radiology---current status and critical review," Diagnostics, vol. 15, no. 3, p. 282, 2025. [15] E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, "Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach," Biometrics, pp. 837-845, 1988. [16] National Comprehensive Cancer Network, "NCCN clinical practice guidelines in oncology: central nervous system cancers," National Comprehensive Care Network, Inc, 2017. [17] M. I. K. Jabed, M. R. M. Sirazy, S. Mandal, S. A. Akter, A. Hassan, and H. Esa, "Developing AI-based financial forecasting and cybersecurity systems for the US digital economy," Frontiers in Computer Science and Artificial Intelligence, vol. 5, no. 5, pp. 30-38, 2026. [18] A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, et al., "A guide to deep learning in healthcare," Nature medicine, vol. 25, no. 1, pp. 24-29, 2019. [19] D. N. Louis, A. Perry, P. Wesseling, D. J. Brat, I. A. Cree, D. Figarella-Branger, et al., "The 2021 WHO classification of tumours of the central nervous system: a summary," Neuro-oncology, vol. 23, no. 8, pp. 1231-1251, 2021. [20] World Health Organization, "Measuring survival, driving change: advancing equity through the WHO Global Initiative for Childhood Cancer," World Health Organization, 2026. [21] Network, N. C. C., Clinical Practice Guidelines in Oncology: Central Nervous System Cancers. [22] M. A. Rahman, R. K. Devnath, S. B. Niloy, C. M. Mehedi, T. H. Chowdhury, and M. I. K. Jabed, "A Stacking Ensemble Framework for Predicting Employee Turnover: Explainable AI with SHAP," in 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS), 2025, pp. 1-6. [23] U. Iqbal, "AI-Driven Predictive Maintenance for U.S. Smart Manufacturing: Deep Learning Models for Equipment Failure Prediction and Operational Resilience," Journal of Engineering and Computational Intelligence Review, vol. 3, no. 1, 2025. [24] U. Iqbal, "AI-Powered Supplier Risk Intelligence: Predicting Financial and Geopolitical Supply Chain Disruptions in U.S. Critical Industries," Journal of Engineering and Computational Intelligence Review, vol. 3, no. 2, 2025. [25] D. Er, "Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach," Biometrics, vol. 44, pp. 837-845, 1988. [26] M. I. K. Jabed, "Stock market price prediction using machine learning techniques," American International Journal of Sciences and Engineering Research, vol. 7, no. 1, pp. 1-6, 2024. [27] A. Al Kium, S. Sarker, S. A. Shikha, M. A. T. Kamal, M. I. K. Jabed, N. K. Munifa, et al., "Health equity and digital disparities in cancer screening and cardiovascular care across socioeconomic and ethnic groups: a systematic review," Vascular and Endovascular Review, vol. 6, no. 2, pp. 35-44, 2023. [28] M. I. K. Jabed, M. A. Manzoor, F. M. Tofa, and M. H. Khan, "Interpretable Ensemble Learning Approach for Breast Cancer Diagnosis Using SHAP-Based Explainable AI," Journal of Computer Science and Technology Studies, vol. 8, no. 8, pp. 244-255, 2026. [29] U. Iqbal, S. Bekmez, and F. A. Qurashi, "Operational risk management through machine learning and business intelligence in US businesses," Spanish Journal of Innovation and Integrity, vol. 54, pp. 239-253, 2026. [30] A. Khan, F. Amin, and U. Imtiaz, "SENTINEL-WHEEL: Entropy-compressed edge intelligence for explainable self-healing cyber defense in connected vehicles," International Journal of Innovative Research, vol. 4, no. 01, pp. 227-237, 2026. [31] F. Amin, U. Imtiaz, and A. Khan, "FALCON-Guard: A lightweight explainable framework for real-time cyber threat detection and adaptive risk mitigation in intelligent driving networks," Multidisciplinary Research in Computing Information Systems, vol. 5, no. 12, pp. 1223-1235, 2025.

Mehran Ali, Zia Ullah, M. Ullah et al. · Journal of Engineering and C... · 0 citations

#edge computing Review Aug 2026

Enhancing Stable Behavioral Imitation through Adaptive Reward Weighting in TD3-SAC-GAIL

The results demonstrate the potential of adaptive reward weighting to provide a systematic mechanism for controlling the exploration–imitation trade-off and enhancing the stability and robustness of GAIL-based policy learning while retaining the exploration advantages of the TD3-SAC hybrid framework.

Mehran Ali, Zia Ullah, Aliza Ashfaq · Journal of Engineering and C... · 0 citations

Review Open access Aug 2026

Dynamic Cognitive Prior Generation for Robust EEG-to-Image Decoding

One of the key challenges in brain computer interfaces (BCIs) is to understand the visual perceptual content (VPC) of non-invasive electroencephalography (EEG) signals with high accuracy, without the aid of brain mapping techniques, which is hindered by high inter-subject variations and the non-stationary nature of neural responses. Current methods, including NeuroBridge, use handcrafted, fixed, subject-agnostic transformations of perceptual variance called Cognitive Prior Augmentation (CPA). These static priors, however, have little capability for modelling the dynamic changes of cognition states and individual brain properties, which severely constrain across-subjects generalization. We introduced Dynamic Cognitive Prior Generation (DCPG) a new framework, which can be learned and is prototype based to adaptively generate priors instead of heuristic augmentations. Our approach distills the subject-specific cognitive priors by modelling the attention to a common bank of prototype representations, based on a given EEG trial and based on a learnable subject embedding. Using feature modulation, DCPG can adaptively calibrate the representation of EEG before semantic projection, thus reducing the domain shift between different subjects effectively. It was shown that DCPG can be used to significantly increase the accuracy of inter-subject retrieval on the THINGS-EEG dataset with an accuracy improvement of +3.0% while adding 4.9% more parameters compared to NeuroBridge. This framework is the new state-of-the-art in the field of robust EEG-image decoding, with results in a variety of populations. REFERENCES [1] W. Zhang, S. Wang, Y. Su, X. Li, C. Zhang, and S. Zhong, "Neurobridge: Bio-inspired self-supervised eeg-to-image decoding via cognitive priors and bidirectional semantic alignment," in Proc. AAAI Conf. Artif. Intell., vol. 40, no. 21, pp. 18028-18036, 2026. [2] K. Ahmed, H. A. Mahmoud, H. M. El Hadad, and Y. Afifi, "Generative AI for the reconstruction of visual stimuli from functional magnetic resonance imaging (fMRI) signals," J. Big Data, 2026. [3] H. Yu, Q. Mu, C. Liu, S. Wang, and J. Sun, "Technical system of electroencephalography-based brain–computer interface: Advances, applications, and challenges," Neural Regener. Res., vol. 21, no. 9, pp. 3885-3907, 2026. [4] X. T. Tran et al., "Inter-and Intra-Subject Variability in EEG: A Systematic Survey," arXiv:2602.01019, 2026. [5] S. Shukla et al., "A survey on bridging EEG signals and generative AI: from image and text to beyond," arXiv:2502.12048, 2025. [6] B. Abibullaev, A. Keutayeva, and A. Zollanvari, "Deep learning in EEG-based BCIs: A comprehensive review of transformer models, advantages, challenges, and applications," IEEE Access, vol. 11, pp. 127271-127301, 2023. [7] M. Hemati, C. Rowley, E. Deem, and L. Cattafesta, "De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets," Theor. Comput. Fluid Dyn., vol. 31, no. 4, 2017. [8] Y. Prabhu, "Unveiling Bias in Multimodal Models," Ph.D. dissertation, 2025. [9] L. Guo and W. Zhang, "Geospatial sentiment analytics for hospitality management: predicting investment returns on service attributes across urban micro-zones," J. Quality Assurance Hospitality Tourism, pp. 1-24, 2026. [10] Y. Ke, D. Liang, and K. Shang, "NeuroAlign: Dynamic Dual-Stream Alignment of Perception and Cognition for Zero-Shot Brain-Image Retrieval," in Proc. 2026 Int. Conf. Multimedia Retrieval, pp. 108-117, 2026. [11] A. F. Nia, "Using Advanced Machine Learning Techniques to Recognize Emotional States from a Hybrid fNIRS-EEG System," Ph.D. dissertation, Univ. Auckland, 2025. [12] U. Iqbal, "AI-driven predictive maintenance for US smart manufacturing: Deep learning models for equipment failure prediction and operational resilience," J. Eng. Comput. Intell. Rev., vol. 3, no. 1, pp. 114-138, 2025. [13] S. Shumba, "Multi-Modal Automated Major Depressive Disorder Detection," Ph.D. dissertation, Stellenbosch Univ., 2025. [14] M. Parvan, "ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics," arXiv:2605.01548, 2026. [15] E. G. Ahsaei, "Constructing and Analyzing Neural Network Dynamics for Information Objectives and Working Memory," Ph.D. dissertation, Washington Univ. St. Louis, 2021. [16] U. Iqbal, "AI-powered supplier risk intelligence: Predicting financial and geopolitical supply chain disruptions in US critical industries," J. Eng. Comput. Intell. Rev., vol. 3, no. 2, pp. 173-193, 2025. [17] P. M. Alamdari, "Multi-Persona Adaptive Recommendation via Cognitive Persona Transitions and Behavioral State Flow Modeling," 2025. [18] R. G. Praveen, P. Cardinal, and E. Granger, "Weakly supervised learning for facial affective behavior analysis: A review," IEEE Trans. Affect. Comput., 2026. [19] U. Imtiaz, F. Amin, and A. Khan, "Crash2Compromise: CrashGuard-FS for Security-Aware Linux File Updates," Multidiscip. Res. Comput. Inf. Syst., vol. 6, no. 2, pp. 294-310, 2026. [20] A. Khan, U. Imtiaz, and F. Amin, "Big Data Analytics in Advanced Cybersecurity: A US Study of Proactive Strategies and Innovative Solutions," Spanish J. Innov. Integrity, vol. 54, pp. 163-180, 2026. [21] S. Hussain, P. K. Jamwal, and P. Van Vliet, "Design synthesis and optimization of a 4-SPS intrinsically compliant parallel wrist rehabilitation robotic orthosis," J. Comput. Des. Eng., vol. 8, no. 6, pp. 1562-1575, 2021. [22] F. Jiang, N. Du, M. Yu, and Q. He, "EmoDNCL+: Dual-stream negative-sample-free contrastive learning with neurophysiological augmentation for EEG emotion recognition," J. King Saud Univ. Comput. Inf. Sci., vol. 38, no. 2, p. 30, 2026. [23] T. Li, Y. Yan, F. Dou, W. Song, and X. Zhang, "Cross-subject generalization for EEG decoding: a survey of deep learning methods," Prog. Biomed. Eng., vol. 8, no. 2, p. 022013, 2026. [24] D. Bailey, "Why Adaptive AI Has No Inner Life: A Structural Account of Function Without Phenomenology," 2026. [25] J. Duan et al., "Critical changes in whole-brain gene networks in response to small-cell lung cancer as revealed by single-nucleus RNA sequencing," Frontiers Immunol., vol. 17, p. 1860628, 2026. [26] U. Iqbal and Y. Bhutto, "Digital transformation through artificial intelligence and advance business analytic in American operational management," J. Theor. Appl. Econometrics, vol. 3, no. 1, pp. 37-50, 2026. [27] U. Iqbal, "AI-enhanced network optimization for electric vehicle charging infrastructure expansion in the United States using graph theory and demand analytics," J. Eng. Comput. Intell. Rev., vol. 2, no. 2, pp. 112-129, 2024. [28] U. Iqbal, S. Bekmez, and F. A. Qurashi, "Operational Risk Management Through Machine Learning and Business Intelligence in U.S. Businesses," Spanish J. Innov. Integrity, vol. 54, pp. 239-253, 2026.

Mehran Ali · Journal of Engineering and C... · 0 citations