Ali Fatahi

Msc in Computer Science

I'm a computer science researcher specializing in deep-learning–driven medical image processing. Using convolutional neural networks, I develop models to detect neurological disorders directly from MRI and SPECT scans. I'm passionate about turning AI breakthroughs into reliable diagnostic tools.

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Technical Skills

My interdisciplinary skill set combines programming, medical imaging, and emerging technologies.

Programming

Extensive experience with Python, MATLAB, and HTML for scientific computing and web development.

Python MATLAB HTML/CSS

Algorithm Design

Specialized in developing efficient algorithms for numerical optimization, medical image analysis, and neural network architecture design.

Optimization neural network architecture design Complexity Analysis

Medical Image Processing

Advanced techniques for MRI/CT analysis including classification, oversampling, registration, and feature extraction.

MRI Analysis Neurological Disorders Classification Feature Extraction

Blockchain Technology

Certified in blockchain fundamentals with experience in decentralized applications and distributed ledger.

Distributed Ledger DApps Blockchain Certification

Featured Project

My most significant research project pushing the boundaries of medical AI.

End-Net: A Deep Neural Network for Early Detection of Multiple Neurological Disorders from MRI

Ongoing

Developed End-Net, a 24-layer deep learning framework that leverages convolutional blocks and optimized Inception modules to extract multi-scale features from MRI scans. End-Net achieves 97.6% accuracy, 95.5% precision, and 97.6% recall for early detection of Alzheimer's, multiple sclerosis, and brain tumor diseases.

Key Features:

97.6% accuracy in multi-class classification of three neurological disorders.
Unified early-stage multi-class classifier for Alzheimer's, Brain Tumor, and MS diseases.
Explainable AI components for clinical interpretability
Processing time under 3 seconds per scan

Deep Learning Medical Imaging Inception Neurological Disorders

Real-time End-Net Research Paper View Code MCND Dataset

97.6%

Detection Accuracy

97.6%

F1 Score

0.1024

Cross-entropy loss

Selected Publications

Publications and preprints from my research.

A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment

Fatahi, A., Zamani, H., Nadimi-Shahraki, MH. (2026). arXiv preprint. Under review.

View the preprint on arXiv

An improved binary quantum-based avian navigation optimizer algorithm to select effective feature subset from medical data: a COVID-19 case study

Fatahi, A., et al. (2024). Journal of Bionic Engineering 21 (1), 426-446.

View on Springer

Binary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical data

Nadimi-Shahraki, MH., Fatahi, A., Zamani, H., Mirjalili, S. (2022). Mathematics 10 (15), 2770.

View on MDPI

MFO-SFR: An enhanced moth-flame optimization algorithm using an effective stagnation finding and replacing strategy

Nadimi-Shahraki, MH., Zamani, H., Fatahi, A., Mirjalili, S. (2023). Mathematics 11 (4), 862.

View on MDPI

View All Publications

Get In Touch

Interested in collaborating or learning more about my research? Feel free to reach out.

Contact Information

Email

fatahi.ali.edu@gmail.com

Address

Islamic Azad University, Najafabad Branch
Computer Engineering Department

Research Collaborations

I'm always interested in discussing potential collaborations with:

Medical institutions with imaging datasets
AI researchers working on medical applications