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PhD, Computer Science

Ahmed Iqbal

Assistant Professor in Information Systems & Data Analytics. Research at the intersection of computer vision, medical imaging, deep learning, and vision transformers.

A’Sharqiyah University, Ibra, Oman

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Google Scholar GitHub LinkedIn ORCID ResearchGate Web of Science
Top 2%
Stanford / Elsevier World Scientists, 2025
1,500+
Citations & counting
70+
Cumulative journal impact factor
40+
ISI-indexed journals reviewed
Portrait of Dr. Ahmed Iqbal

fig. 1 — Ahmed Iqbal · face detection

About

From medical scans to model architectures

Dr. Ahmed Iqbal is an Assistant Professor in Information Systems and Data Analytics at A’Sharqiyah University, Ibra, Oman. He previously worked as a Postdoctoral Fellow at Hamad Bin Khalifa University in Doha, Qatar, and as an Assistant Professor in the Department of Computing at Sir Syed CASE Institute of Technology in Islamabad, Pakistan. He has been recognized in the Stanford University & Elsevier Global List of the World’s Top 2% Scientists (2025). He earned his Ph.D. in Computer Science from COMSATS University Islamabad in 2024, under the supervision of Prof. Dr. Muhammad Sharif Malik.

His primary research interests include computer vision, medical imaging, deep learning, and vision transformers. Dr. Iqbal has published extensively in top-tier (Q1) journals such as Knowledge-Based Systems, Expert Systems with Applications, Engineering Applications of Artificial Intelligence, and Journal of King Saud University — Computer and Information Sciences. Proficient in Python and MATLAB, he works across PyTorch, TensorFlow, OpenCV, and LaTeX.

2020 – 2024
COMSATS University Islamabad
Ph.D., Computer Science
2024 – 2025
Hamad Bin Khalifa University
Postdoctoral Fellow · Doha, Qatar
2024
Sir Syed CASE Institute of Tech.
Assistant Professor · Islamabad
2025 – now
A’Sharqiyah University
Assistant Professor · Oman
Open-source code
PDF-UNet FF-UNet MDA-Net TB-DenseNet MET-Net CPS-Former
Public datasets — Mendeley Data
BUS_UC BUSC BUS Synthetic Dataset

Research program

Clinically deployable AI for medical imaging

My research designs deep-learning systems that segment, classify, and detect disease from ultrasound, radiography, and surgical video. The throughline across every project: precise, data-efficient architectures that hold up on real clinical data — and ship as open source.

01 · Segmentation

Medical image segmentation

Encoder–decoder and U-shaped architectures engineered for precise lesion and structure delineation in breast ultrasound and multimodal biomedical imaging.

MDA-Net FF-UNet PDF-UNet
02 · Transformers

Vision transformers for clinical AI

Adapting self-attention and Swin transformers to segment and classify breast cancer, colorectal polyps, and surgical instruments with multi-scale precision.

BTS-ST MET-Net CPS-Former SurgiFormer
03 · Diagnosis

Detection & diagnosis from radiographs

Hybrid CNN pipelines for tuberculosis, pneumonia, and lung-disease screening from chest X-rays — built for accuracy under noisy, real-world acquisition.

TB-DenseNet TBXNet
04 · Data-efficient learning

Semi-supervised & generative learning

Semi-supervised training, synthetic data, and GANs that let high-performing models learn from limited annotated medical images — alongside public datasets.

GAN Survey PDF-UNet BUS Synthetic

Recent updates

What’s new

● milestone   ○ update

06 / 2026
Our paper “AttGRU-based intra-site and cross-site building energy consumption prediction using meteorological features with uncertainty-aware CO2 scenario analysis” has been published in Sustainable Energy Technologies and Assessments. LinkedIn
04 / 2026
Our paper “SurgiFormer: A Multi-Scale Vision Transformer for Precise Surgical Instrument Segmentation” has been published in Computers and Electrical Engineering. LinkedIn
10 / 2025
Joined A’Sharqiyah University as Assistant Professor in the Department of Information Systems & Business Analytics.
09 / 2025
Included in the Stanford University & Elsevier Global List of the World’s Top 2% Scientists (2025). LinkedIn
05 / 2025
Highlights from the AutismTech Conference 2025, hosted by the College of Science and Engineering at Hamad Bin Khalifa University. LinkedIn
04 / 2025
A proud milestone: 1,000 citations and counting. LinkedIn
12 / 2024
Joined Hamad Bin Khalifa University, Doha, Qatar as a Postdoctoral Researcher. Picture

Selected publications

Peer-reviewed research

Selected first-author work in Q1 journals, every paper paired with open-source code or data. Full list on Google Scholar.

Publication impact — journal impact factorselected first-author papers
JKSU–CIS13.4
Expert Syst. Appl.8.6
Knowl.-Based Syst.8.1
Eng. Appl. AI8.0
Eng. Appl. AI7.5
Biomed. Sig. Proc.5.0
Cognitive Comput.4.8
Tuberculosis2.9
IJMIR2.5

Rethinking encoder–decoder architecture using vision transformer for colorectal polyp and surgical instruments segmentation

Ahmed Iqbal, Zohair Ahmed, Muhammad Usman, Isra Malik

Engineering Applications of Artificial Intelligence

7.5Impact
Paper Code

Memory-efficient transformer network with feature fusion for breast tumor segmentation and classification

Ahmed Iqbal, Muhammad Sharif

Engineering Applications of Artificial Intelligence

8.0Impact
Paper Code

Tuberculosis chest X-ray detection using CNN-based hybrid segmentation and classification approach

Ahmed Iqbal, Muhammad Usman, Zohair Ahmed

Biomedical Signal Processing and Control

5.0Impact
Paper Code

PDF-UNet: A semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network

Ahmed Iqbal, Muhammad Sharif

Expert Systems with Applications

8.6Impact
Paper Code

BTS-ST: Swin transformer network for segmentation and classification of multimodality breast cancer images

Ahmed Iqbal, Muhammad Sharif

Knowledge-Based Systems

8.1Impact
Paper

MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

Ahmed Iqbal, Muhammad Sharif

Journal of King Saud University — Computer and Information Sciences

13.4Impact
Paper Code

An efficient deep learning-based framework for tuberculosis detection using chest X-ray images

Ahmed Iqbal, Muhammad Usman, Zohair Ahmed

Tuberculosis

2.9Impact
Paper

Generative adversarial networks and their applications in biomedical image segmentation: a comprehensive survey

Ahmed Iqbal, Muhammad Sharif, Mussarat Yasmin, Mudassar Raza, Shabib Aftab

International Journal of Multimedia Information Retrieval

2.5Impact
Paper

FF-UNet: A U-shaped deep convolutional neural network for multimodal biomedical image segmentation

Ahmed Iqbal, Muhammad Sharif, Muhammad Attique Khan, Wasif Nisar, Majed Alhaisoni

Cognitive Computation

4.8Impact
Paper Code

Teaching

Courses

Digital Image Processing Machine Learning Deep Learning Software Engineering Digital Logic Design Cloud Computing

Supervision

Student theses

A Robust Scheme for Detection of Lung Diseases Using Deep Learning
Nauman Iftikhar (1812134), Hira Adnan (1812115) · 2022 · SZABIST, Islamabad
Diagnosis of Tuberculosis Using a Deep Learning Convolutional Neural Network
Asim Qamar (1880145), Muhammad Sayyam Khan (1880126) · 2022 · SZABIST, Islamabad
Diagnosis of Pneumonia Using a Deep Learning CNN
Saqib Amin (1912273), Sajjad Karim (1912272), Zain Habib (1912283) · 2023 · SZABIST, Islamabad

Editorial service

Journal reviewing

Reviewer for 40+ ISI-indexed journals across Elsevier, Springer, IEEE, Wiley, Nature, and others. Verified certificates linked where available.

Knowledge-Based Systems
Elseviercert Applied Soft Computing
Elseviercert Computers and Electrical Engineering
Elseviercert Biomedical Signal Processing & Control
Elseviercert Artificial Intelligence Review
Springercert Scientific Reports
Naturecert
IEEE Trans. on Neural Networks & Learning Systems
IEEE
Journal of Biomedical and Health Informatics
IEEE
IEEE Transactions on Artificial Intelligence
IEEE
Cognitive Computation
Springercert Machine Vision and Applications
Springercert The Visual Computer
Springercert

Get in touch

Open to collaboration

Interested in research collaboration, reviewing, or supervision in computer vision and medical imaging? I’m glad to hear from you.

Email me
Email
Ahmed.iqbal [at] ieee.org
Office
A’Sharqiyah University, Ibra, Oman