Mohamed Mouhafid

Mohamed Mouhafid

Machine Learning Researcher
Morocco
English, Chinese, Arabic
Internship On-site

نبذة عني

I am a Machine Learning Researcher with a PhD in Electronic Science and Technology from Hebei University of Technology, Tianjin, China, where I also earned my Master’s degree in Communication and Information Systems. Dur…

الخبرة

PhD Researcher — Medical Image Analysis

Hebei University of Technology · Tianjin, China
Jan 2022 - Jan 2026 · 4 سنوات

<p>Conducted research in deep learning, computer vision, and medical image analysis, with a primary focus on retinal fundus image analysis. Developed and evaluated deep learning models for automated retinal disease detection and classification, incorporating image preprocessing, feature extraction, and model optimization techniques. Investigated convolutional neural networks, transfer learning, and advanced image processing methods to improve diagnostic accuracy, computational efficiency, and model reliability in medical imaging applications.</p>

Research Intern — Medical Imaging

Children’s Hospital of Hebei · Shijiazhuang, China
Dec 2025 - Dec 2025 · 22 أيام

<p>Assisted in the analysis of retinal imaging data acquired using Spectralis Optical Coherence Tomography (OCT), including reviewing OCT scans, retinal thickness maps, and retinal images. Supported the examination of retinal structures, assessment of retinal layer thickness, identification of imaging abnormalities, and interpretation of imaging findings to support clinical research and image analysis.</p>

IT Intern

Province of Berrechid, Dept. of IT · Berrechid, Morocco
Mar 2016 - Apr 2016 · 26 أيام

<p>Designed and implemented a prototype web-based employee management platform to support the digital management of employee information and administrative records. </p>

المشاريع

Towards Efficient Glaucoma Screening with Convolution-Involution

Hebei university of technology · www.doi.org/10.7717/peerj-cs.2844
المدة : 01-Oct-2024 - 01-Jan-2025

<p>Mouhafid M, Zhou Y, Shan C, & Xiao Z. (2025). Towards Efficient Glaucoma Screening with Modular Convolution-Involution Cascade Architecture. PeerJ Computer Science, 11: e2844. (Q2, SCI-indexed) DOI: 10.7717/peerj-cs.2844</p><p></p><p>This study introduces the Modular Convolution-Involution Cascade Network (MCICNet), an innovative CNN architecture designed to address these challenges in the context of glaucoma</p><p>detection. The model employs a combination of convolution and involution operations in a cascade structure, allowing for the effective capture of inter-channel dependencies within the feature extraction process. Furthermore, the classification phase integrates light gradient boosting machine (LightGBM) as a replacement for traditional FC layers, offering enhanced precision and generalization while reducing model complexity. Extensive experiments conducted on the LAG and ACRIMA datasets demonstrate that MCICNet achieves significant improvements compared to existing CNN and transformer-based models. The model attained a classification accuracy of 95.6% on the LAG dataset and 96.2% on ACRIMA, outperforming nine widely used CNN architectures (AlexNet, MobileNetV2, SqueezeNet, ResNet18, GoogLeNet, DenseNet121, EfficientNetB0, ShuffleNet, and VGG16), as well as three transformer-based models (ViT, MaxViT, and SwinT). Additionally, MCICNet showed superior performance over its variant without involution (MCICNetNoInvolution). With only 0.9 million parameters, MCICNet demonstrates substantial efficiency in resource utilization alongside its high learning capability, establishing it as an advanced and computationally efficient solution for glaucoma detection.</p><p></p>

الشهادات

Outstanding International Graduate

Hebei University of Technology, China (PRC) · Tianjin , Tianjin , China · 2026

المهارات

Python OpenCV NumPy Pandas Matplotlib SciPy TensorFlow Keras PyTorch Torchvision CNNs Vision Transformer segmentation networks classical ML SQL database administration software engineering web application development mobile application development
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