PhD Scholar, at Islamia College Peshawar,Pakistan
Mr. Siraj Khan is a dedicated researcher and educator specializing in digital image processing and medical image analysis. He is currently affiliated with the Digital Image Processing Lab at Islamia College, Peshawar, Pakistan. Mr. Khan holds a Ph.D. in Computer Science, focusing on the detection and classification of cells in blood smear images using Convolutional Neural Networks (CNNs). His research interests span deep learning applications in medical imaging, IoT, bioinformatics, and smart healthcare services. With expertise in Python, MATLAB, and various deep learning frameworks like TensorFlow and PyTorch, Mr. Khan is passionate about deploying research to improve healthcare technologies in smart cities. His work has been published in several high-impact journals, contributing significantly to the fields of medical diagnostics and artificial intelligence.
Professional Profile
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Education 🎓
Mr. Siraj Khan is a highly skilled researcher and educator, currently affiliated with the Digital Image Processing Lab at Islamia College, Peshawar, Pakistan. He holds a Ph.D. in Computer Science, specializing in medical image processing, from Islamia College, Peshawar (2018–2021). His research for his Ph.D. focused on “Detection and Classification of Cells in Blood Smear Images using Convolutional Neural Networks.” He also holds a Master’s degree in Computer Vision (2014–2016) from the same institution, where he developed a resource-aware framework for leucocyte segmentation in blood smear images. Earlier, Mr. Khan completed his MCS (Computer Science) at the Federal Urdu University of Arts Science & Technology, Karachi (2008–2010), focusing on computer vision.
Experience đź’Ľ
Mr. Khan is currently a full-time researcher at the Digital Image Processing Laboratory (DIP Lab) at Islamia College, Peshawar. He has led numerous research projects related to medical image segmentation, classification, and detection, applying deep learning tools like TensorFlow, Keras, and PyTorch. In addition, Mr. Khan has collaborated with various research groups internationally, enhancing the scope and impact of his work. His work on healthcare services for smart cities reflects his ability to bridge the gap between academia and practical, deployable technology.
Research Interest 🔬
Mr. Khan’s research primarily revolves around deep learning applications in medical image analysis, focusing on the detection, segmentation, and classification of cells in blood smear images. His work employs advanced techniques such as Convolutional Neural Networks (CNNs) and dual attention networks for efficient leukocyte detection. His research also extends to smart healthcare services in smart cities, leveraging IoT technologies and bioinformatics to enhance healthcare systems. Additionally, Mr. Khan is passionate about deploying his research in real-world applications, using frameworks like TensorFlow, PyTorch, and MATLAB.
Award 🏅
Mr. Khan’s research excellence is evident in his contributions to the fields of medical image processing and smart healthcare. Although specific honors are not listed, his work has received significant academic recognition through publications in high-impact journals and conference proceedings. His contributions to the development of healthcare frameworks, including the Raspberry Pi-assisted face recognition system for law enforcement in smart cities, have been acknowledged by peers in the academic community.
Top Noted Publication đź“‘
Raspberry Pi Assisted Face Recognition Framework for Enhanced Law-Enforcement Services in Smart Cities
Authors: M Sajjad, M Nasir, K Muhammad, S Khan, Z Jan, AK Sangaiah, …
Citations: 241
Index: Future Generation Computer Systems
Year of Publication: 2020
Brain Tumor Segmentation Using K-means Clustering and Deep Learning with Synthetic Data Augmentation for Classification
Authors: AR Khan, S Khan, M Harouni, R Abbasi, S Iqbal, Z Mehmood
Citations: 218
Index: Microscopy Research and Technique
Year of Publication: 2021
Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities
Authors: M Sajjad, S Khan, Z Jan, K Muhammad, H Moon, JT Kwak, S Rho, …
Citations: 131
Index: IEEE Access
Year of Publication: 2016
A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images
Authors: S Khan, M Sajjad, T Hussain, A Ullah, AS Imran
Citations: 107
Index: IEEE Access
Year of Publication: 2020
Computer Aided System for Leukocytes Classification and Segmentation in Blood Smear Images
Authors: M Sajjad, S Khan, M Shoaib, H Ali, Z Jan, K Muhammad, I Mehmood
Citations: 24
Index: 2016 International Conference on Frontiers of Information Technology (FIT)
Year of Publication: 2016
Conclusion
Mr. Siraj Khan exhibits the qualities of a strong contender for the Best Researcher Award. His exceptional contributions to medical image processing, particularly in the use of deep learning for blood smear analysis, are groundbreaking and highly relevant in the healthcare sector. His technical abilities, combined with a proven track record of high-impact publications, make him an ideal candidate for this award. Expanding his research into other cutting-edge areas of AI and increasing his research visibility through international collaborations and media could elevate his career further.