Negar Abbasi

Research project: Enhancing Pulmonary Disease Diagnosis with Deep Learning: A Path to Precision Medicine


Deep learning techniques are revolutionizing the field of medical imaging, especially in enhancing the diagnosis of pulmonary diseases such as tuberculosis. These methods facilitate timely and accurate disease detection, crucial for effective treatment. Despite their success, challenges persist, particularly in handling complex chest X-ray (CXR) images where infections complicate image analysis. Current deep learning models struggle with capturing both local and global feature relationships and are often hindered by poor data quality, leading to lower accuracy in disease classification. This PhD project aims to tackle these issues by developing robust deep learning approaches that improve pulmonary disease classification and contribute to the broader implementation of deep learning models in the field.

  • Research degree: PhD
  • Title of project: Enhancing Pulmonary Disease Diagnosis with Deep Learning: A Path to Precision Medicine
  • Research supervisor: Dr Tariq Rahim
  • Other research supervisor: Professor Nada Philip


I am Negar Abbasi, a PhD student focused on medical image processing through my project, "Enhancing Pulmonary Disease Diagnosis with Deep Learning: A Path to Precision Medicine." This research aims to revolutionize the accuracy and efficiency of diagnosing lung diseases, utilizing advanced deep learning technologies.

Previously, I earned a Master's degree in Data Science from Kingston University, London, where I graduated with distinction in 2023. My dissertation explored the potential of deep learning for recognizing human emotions through facial expressions.

In addition to my data science expertise, I also hold a Master's degree in Software Engineering from Tehran University. My comprehensive training in software engineering complements my data science skills, enabling me to effectively address complex technological challenges.

Areas of research interest

  • Machine Learning
  • Deep Learning
  • Image Processing
  • Artificial intelligence


  • MSc in Data Science, Kingston University London
  • MSc in Software Engineering, University of Tehran

Funding or awards received

  • PhD studentship, Graduate Research School, Kingston University London


Abbasi, N., Soltanaghaei, M. and Zamani Boroujeni, F., 2023. Anomaly detection in IOT edge computing using deep learning and instance-level horizontal reduction. The Journal of Supercomputing, pp.1-31.

Abbasi, N., Moeini, A. and Gandomani, T.J., 2018. Web Service Candidate Identification Using the Firefly Algorithm. International Journal of Web Services Research (IJWSR), 15(4), pp.45-60.

Golnoori, F., Abbasi, N., Jahangard, S. and Boroujeni, F.Z., Medical Image Retrieval Based On Ensemble Clustering.