Faculty Img
  • Phone:

    +966-13-849-5414

  • Email:

    akhan@pmu.edu.sa

  • Office No:

    S-008

  • Adil Humayun Khan, Ph.D.

  • Job Title :

    Instructor

  • College :

    College of Engineering


  • Department :

    Electrical Engineering


I did my Ph.D. in Computer Science and Information Technology from UNIVERSITI MALAYSIA SARAWAK, Malaysia. My research is in image segmentation and classification using machine and deep learning. In my Ph.D. thesis, I have proposed statistical, traditional machine, and deep learning-based models to segment and classify skin lesions from dermoscopic images. I did my MS in Electrical Engineering from KFUPM, Saudi Arabia, where my area of research was signal processing. My BS is in Telecommunication Engineering from COMSATS University, Lahore, Pakistan. I secured the Institute and Campus Gold Medal in my undergraduate degree. 

 

 

2018 – 2022                Ph.D. in Computer Science and Information Technology

                                    UNIVERSITI MALAYSIA SARAWAK- Malaysia

Field of Research: Visual Information Technologies

                                               

2011 – 2013                Masters in Electrical Engineering

                                    King Fahd University of Petroleum and Minerals - Dhahran, Saudi Arabia

                                                CGPA: 3.82/4.00

 

2005 – 2008                Bachelors in Electrical (Telecommunication) Engineering

                                    COMSATS University Jinnah Campus- Lahore, Pakistan

                                                CGPA: 3.71/4.00                                  

(Secured Campus and Institute Gold Medal)

 

 

Journal Publications

[J-10]   Gaussian-Filtered High-Frequency-Feature Trained Optimized BiLSTM Network for Spoofed-Speech Classification. Sensors. 2023 Jul 24;23(14):6637.

[J-9]     Ensemble Learning of Deep Learning and Traditional Machine Learning Approaches for Skin Lesion Segmentation and Classification. Concurrency and Computation: Practice and Experience 2022 Jun 10;34(13):e6907.

[J-8]     Statistical Feature Learning through Enhanced Delaunay Clustering and Ensemble Classifiers for Skin Lesion Segmentation and Classification, International Journal of Imaging Systems and Technology. Journal of Theoretical and Applied Information Technology. 2021 Mar 15;99(5).

[J-7]     Classification of skin lesion with hair and artifacts removal using black-hat morphology and total variation. International Journal of Computing and Digital Systems. 2021 May 2;10:597-604.

[J-6]     Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation, Current Medical Imaging, 2021 Jan 3.

[J-5]     QR based Despeckling Approach for Medical Ultrasound Images, Current Medical Imaging, 2019 15, no. 7: 679-688.

[J-4]     Multi-channel Convolutions Neural Network Based Diabetic Retinopathy Detection from Fundus Images, Procedia Computer Science. 2019 Jan 1;163:283-91.

[J-3]     A Hybrid Particle Swarm Optimization Technique for Adaptive Equalization, Arabian Journal for Science and Engineering, 2018, pp 1-8

[J-2]     Deep CNN based MR image denoising for tumor segmentation using watershed transform, International Journal of Engineering and Technology, International Journal of Engineering & Technology, 2018 7, no. 2.3 : 37-42.

[J-1]     Speckle Suppression in Medical Ultrasound Images through Schur Decomposition, IET Image Processing 12, 2017, no. 3: 307-313.

 

Conference Publications

[C-11] Landscape Change Detection Using Auto-optimized K-means Algorithm, 3rd IEEE International Symposium on Advanced Electrical and Communication Technologies IEEE-ISAECT, Morocco 2020.

[C-10] Spectral Decomposition By Schur for Medical Ultrasound Image Denoising, IEEE International Symposium on Advanced Electrical and Communication Technologies (ISASET), Rome, Italy, November 2019

[C-9]    Ultrasound Image Denoising Using Orthogonal Decomposition in Frequency Domain, 9th IEEE International Conference on System Engineering and Technology (ICSET), Malaysia, October 2019.

[C-8]    Multi-channel Convolutions Neural Network based Diabetic Retinopathy Detection from Fundus images, 16th Learning and Technology Conference 2019, Effat University Jeddah, Saudi Arabia

[C-7]    Segmentation of Melanoma Skin Lesions using Anisotropic Diffusion and Adaptive Thresholding, 8th International Conference on Biomedical Engineering and Technology (ICBET 2018) Proceedings, Indonesia

[C-6]    QR based De-noising Scheme for Medical Ultrasound Images, 9th IEEE GCC Conference & Exhibition, May 2017, Manama, Bahrain.

[C-5]    Automatic Multimodal Brain Image Classification using MLP and 3D Glioma Tumor Reconstruction, 9th IEEE GCC Conference & Exhibition, May 2017, Manama, Bahrain.

[C-4]    Multiclass Brain Glioma Tumor Classification using Block-based 3D Wavelet Features of MR Images, 4th International Conference on Electrical and Electronics Engineering, April 2017, Ankara, Turkey.

[C-3]    IoT based Real-time Voice Analysis and Smart Monitoring System for Disabled People, 1st International Conference on Advanced Research (ICAR -2017), Bahrain.

[C-2]    Eye Click: Eye Gazed Based User Interface for the Disabled People, International Conference on Technology for Helping People with Special Needs, ICTHP-2013.

[C-1]    Application of a PSO algorithm in adaptive equalization using adaptive inertia weights, The Fourth Scientific Conference for Students of Higher Education in K.S.A, 2013.

 

Google Scholar Link

 

https://scholar.google.com/citations?user=j0FDz1UAAAAJ&hl=en

 

Research Gate Link

https://www.researchgate.net/profile/Adil-Khan

 

 

 

 

My research interests are in Signal and image processing, currently I am an active member of “BIOMEDICAL SIGNAL/IMAGE PROCESSING” research group in PMU. I have done work on image despeckling, segmentation and classification.

 

 

IEEE