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OUR TEAM

Ms. Kiran Jabeen

Lecturer (CS)

Introduction

I am Kiran Jabeen, Lecturer in the Department of Computer Science at NUTECH University. With extensive experience in academia and research, my primary interests lie in Artificial Intelligence (AI), Machine Learning, and Computer Vision, with a strong focus on healthcare applications. I am deeply committed to leveraging data-driven models to address critical challenges, particularly in breast cancer diagnosis and medical imaging. My teaching philosophy emphasizes building a solid theoretical foundation while equipping students with practical, hands-on skills to thrive in the rapidly evolving tech industry.

Experience

Lecturer (Visiting), Department of Computer Science, F. G. Post Graduate College, Wah Cantt (2024)

    • Teach undergraduate and graduate courses in Computer Organization and Design, Computer Networks, Software Engineering and Software Quality Assurance.
    • Advise undergraduate students on Final Year projects related to AI and computer vision.

Research Associate, HITEC University, Taxila (2021–2024)

  • Conducted research on medical imaging, publishing multiple papers and advancing the field with novel findings.
  • Developed innovative models and algorithms tailored to medical imaging, enhancing diagnostic accuracy and efficiency.
  • Customized and fine-tuned pre-trained models to address unique challenges in medical imaging data analysis, improving model performance significantly.

Qualification

  • Academia:
    • Ph.D. in Computer Science, HITEC University, Taxila (in progress)
    • MS. in Computer Science, HITEC University, Taxila (2019-2021)
    • MIT. in Information Technology, PMAS Arid Agriculture University, Rawalpindi (2016-2018)
    • B.Sc. (Major Subjects: Maths, Physics, Computer Science), University of Punjab (2014-2016)

I have authored 9 peer-reviewed publications in high-impact journals and conferences, focusing on the application of deep learning and AI in medical imaging. My research includes innovative frameworks for breast cancer diagnosis, such as the integration of deep learning models and advanced optimization techniques, published in renowned venues like Sensors, Diagnostics, Frontiers in Oncology, and Engineering Applications of Artificial Intelligence. I also serve as a reviewer for prominent academic journals, reflecting my active involvement in the scientific community and expertise in AI and machine learning.

  • Certifications:
    • Certified E-commerce, Online E-commerce, e-rozgaar  (2022)
  • Trainings
    • Office Automation, Margalla Training Institute (2011)

Taught Courses

I have taught a variety of undergraduate courses in the Computer Science Department. Below are the courses I have been involved in teaching:

  • Computer Networks (CS208)
    A fundamental course that examines networking principles, including network architecture, protocols, IP addressing, subnetting, and technologies like Ethernet, wireless, and the internet.
  • Computer Organization and Design (CS210)
    A course providing an in-depth understanding of computer hardware, including topics like Number system, binary Codes, K- maps, Adders, Multiplexers, processor architecture, memory systems, instruction set design, and performance optimization etc,
  • Software Engineering (CS310)
    A course focused on the principles and practices of software development, covering the software development lifecycle, requirements analysis, system design, and project management.
  • Software Quality Assurance (CS312)
    A specialized course emphasizing techniques and methodologies for ensuring software quality, including testing strategies, verification, validation, and quality control frameworks.
  • Design and Analysis of Algorithms (CS320)
    A detailed course on algorithmic design strategies such as divide-and-conquer, greedy algorithms, and dynamic programming, with a focus on complexity analysis and optimization techniques.

Research

My research focuses on the integration of machine learning and computer vision, with a strong emphasis on healthcare applications. I am currently working on projects aimed at advancing breast cancer diagnosis and treatment, including developing algorithms for automated detection and personalized therapy recommendations. My work involves exploring advanced techniques such as Vision Transformers and self-attention mechanisms in medical imaging. Additionally, my PhD thesis is dedicated to deep learning-based approaches for breast cancer detection.

    • Ongoing Research Projects:
      • AI for early-stage cancer detection using computer vision
      • Predictive analytics for Breast Cancer disorders using machine learning
    • Publications:
      • Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y. D., Hamza, A., ... & Damaševičius, R. (2022). Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors, 22(3), 807.
      • Jabeen, K., Khan, M. A., Balili, J., Alhaisoni, M., Almujally, N. A., Alrashidi, H., ... & Cha, J. H. (2023). BC2NetRF: breast cancer classification from mammogram images using enhanced deep learning features and equilibrium-jaya controlled regula falsi-based features selection. Diagnostics, 13(7), 1238.
      • Jabeen, K., Khan, M. A., Hameed, M. A., Alqahtani, O., Alouane, M., & Masood, A. (2024). A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images. Frontiers in Oncology, 14, 1347856.
      • Jabeen, K., Khan, M. A., Damaševičius, R., Alsenan, S., Baili, J., Zhang, Y. D., & Verma, A. (2024). An intelligent healthcare framework for breast cancer diagnosis based on the information fusion of novel deep learning architectures and improved optimization algorithm. Engineering Applications of Artificial Intelligence, 137, 109152.
      • Jabeen, K., Khan, M. A., Hamza, A., Albarakati, H. M., Alsenan, S., Tariq, U., & Ofori, I. (2024). An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images. CAAI Transactions on Intelligence Technology.
      • Meer, M., Khan, M. A., Jabeen, K., Alzahrani, A. I., Alalwan, N., Shabaz, M., & Khan, F. (2024). Deep convolutional neural networks information fusion and improved whale optimization algorithm based smart oral squamous cell carcinoma classification framework using histopathological images. Expert Systems, e13536.
      • Rauf, F., Khan, M. A., Bashir, A. K., Jabeen, K., Hamza, A., Alzahrani, A. I., ... & Masood, A. (2023). Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes. Frontiers in Medicine, 10, 1330218.
      • Khan, M. A., Mir, M., Ullah, M. S., Hamza, A., Jabeen, K., & Gupta, D. (2023, November). A fusion framework of pre-trained deep learning models for oral squamous cell carcinoma classification. In International Conference on Computing and Communication Networks (pp. 769-782). Singapore: Springer Nature Singapore.
      • Rauf, F., Attique Khan, M., Albarakati, H. M., Jabeen, K., Alsenan, S., Hamza, A., ... & Nam, Y. (2024). Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks. Frontiers in Medicine, 11, 1486995.