Artificial Intelligence Scientist with 5+ years of experience in deep learning and machine learning across academic and industrial projects. Skilled in designing, developing, and deploying AI solutions with expertise in federated learning, computer vision, NLP, medical imaging, and robotics. Author of 20+ peer-reviewed publications, advancing methods in healthcare, robotics, and industrial AI. Passionate about developing scalable, real-world AI applications and driving innovation at the intersection of research and engineering.
[1] M. Chetoui and M. A. Akhloufi, “Stacking Ensemble Learning for Accurate Polyp Segmentation,” in Proc. 2025 6th Int. Conf. Bio-engineering for Smart Technologies (BioSMART), May 2025, pp. 1–5.
[2] M. Chetoui and M. A. Akhloufi, "A Novel Ensemble Meta-Model for Enhanced Retinal Blood Vessel Segmentation Using Deep Learning Architectures," Biomedicines, vol. 13, no. 1, p. 141, 2025.
[3] M. Chetoui and M. A. Akhloufi, "Fire and smoke detection using fine-tuned YOLOv8 and YOLOv7 deep models," Fire, vol. 7, no. 4, p. 135, 2024.
[4] M. Chetoui and M. A. Akhloufi, "Enhancing Fish Detection and Classification in Sonar Images Through Deep Learning," in OCEANS 2024-Halifax, IEEE, 2024.
[5] M. Chetoui and M. A. Akhloufi, ‘Peer-to-peer federated learning for COVID-19 detection using transformers’, Computers, vol. 12, no. 5, p. 106, 2023.
[6] M. Chetoui, M. A. Akhloufi, E. M. Bouattane, J. Abdulnour, S. Roux, and C. D. Bernard, ‘Explainable COVID-19 detection based on chest x-rays using an end-to-end RegNet architecture’, Viruses, vol. 15, no. 6, p. 1327, 2023.
[7] M. Chetoui and M. A. Akhloufi, ‘Object detection model-based quality inspection using a deep CNN’, in Sixteenth International Conference on Quality Control by Artificial Vision, 2023, vol. 12749, pp. 65–72.
[8] M. Chetoui and M. A. Akhloufi, ‘Federated Learning for Diabetic Retinopathy Detection Using Vision Transformers’, BioMedInformatics, vol. 3, no. 4, pp. 948–961, 2023.
[9] M. Chetoui and M. A. Akhloufi, ‘Explainable vision transformers and radiomics for covid-19 detection in chest x-rays’, Journal of Clinical Medicine, vol. 11, no. 11, p. 3013, 2022.
[10] M. Chetoui and M. A. Akhloufi, ‘Deep efficient neural networks for explainable COVID-19 detection on CXR images’, in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2021, pp. 329–340.
[11] A. Traoré, M. Chetoui, F.-G. Landry, and M. A. Akhloufi, ‘Ensemble Learning Framework to Detect Partial Discharges and Predict Power Line Faults’, in 2021 IEEE Electrical Power and Energy Conference (EPEC), 2021, pp. 285–289.
[12] M. Chetoui, M. A. Akhloufi, B. Yousefi, and E. M. Bouattane, ‘Explainable COVID-19 detection on chest X-rays using an end-to-end deep convolutional neural network architecture’, Big Data and Cognitive Computing, vol. 5, no. 4, p. 73, 2021.
[13] M. Chetoui and M. A. Akhloufi, ‘Automated Detection of COVID-19 Cases using Recent Deep Convolutional Neural Networks and CT images’, in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, pp. 3297–3300.
[14] M. Chetoui and M. A. Akhloufi, ‘Efficient deep neural network for an automated detection of COVID-19 using CT images’, in 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 1769–1774.
[15] M. Chetoui and M. A. Akhloufi, ‘Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets’, Journal of Medical Imaging, vol. 7, no. 4, pp. 044503–044503, 2020.
[16] M. Chetoui and M. A. Akhloufi, ‘Deep retinal diseases detection and explainability using OCT images’, in International Conference on Image Analysis and Recognition, 2020, pp. 358–366.
[17] M. Chetoui, M. A. Akhloufi, and M. Kardouchi, ‘Diabetic retinopathy detection using machine learning and texture features’, in 2018 IEEE Canadian conference on electrical & computer engineering (CCECE), 2018, pp. 1–4.