Publications

Patents:

1. Na Gong, William Oswald, Jinhui Wang, Mohamed Shaban, and Md. Bipul Hossain, “Machine Learning Processing Using Flexible Bit Truncation”, Utility – Non-Provisional US Patent Application, No. 19/304,648. Filed in August 2025. 

Book Chapters:

1. Estate Sokhadze, Mohamed Shaban, Ayman El-Baz, Allan Tasman, Christopher Stewart, and Rex Cannon, “Application of electroencephalogram and other neurophysiological measures in assessment of cue reactivity in individuals with substance use disorder”, in Introduction to Quantitative EEG and Neurofeedback, 3rd Edition, Elsevier, pp. 309-322, 2023.

2. Estate Sokhadze, Mohamed Shaban, Ayman El-Baz, Allan Tasman, Lonnie Sears, and Manuel Casanova, “Event-related potentials and gamma oscillations in EEG as functional diagnostic biomarkers and outcomes in autism spectrum disorder treatment research”, in Neural Engineering Techniques for Autism Spectrum Disorder, 1st Edition, Vol. 2, Elsevier, pp. 297-319, 2023. 

3. Mohamed Shaban, Ali Mahmoud, Ahmed Shalaby, Mohammed Ghazal, Harpal Sandhu, and Ayman El-Baz, “Low-Complexity Computer-Aided Diagnosis for Diabetic Retinopathy”, in Diabetes and Retinopathy, Elsevier, Vol. 2, pp. 133-149, 2020. 

Peer Reviewed Journal Articles:

1. Md. Bipul Hossain, and Mohamed Shaban, “Resource Efficient Attention Guided Deep Learning for Histopathology Based Lung and Colon Cancer Classification”, Signal, Image and Video Processing, Vol. 19, 1311, 2025. https://doi.org/10.1007/s11760-025-04891-1

2. Md. Bipul Hossain, Na Gong, and Mohamed Shaban, “Novel Channel Attention-Based Filter Pruning Methods for Low-Complexity Semantic Segmentation Models”, Machine Learning with Applications, Vol. 21, 100725, 2025. https://doi.org/10.1016/j.mlwa.2025.100725

3. Md. Bipul Hossain, Na Gong, and Mohamed Shaban, “A Novel Attention-Based Layer Pruning Approach for Low-Complexity Convolutional Neural Networks”, Advanced Intelligent Systems, Vol. 6, no. 6, 2400161, 2024. https://doi.org/10.1002/aisy.202400161  

4. Mohamed Shaban, “A Novel Variational Mode Decomposition Based Convolutional Neural Network for the Identification of Freezing of Gait Intervals for Patients with Parkinson’s Disease”, Machine Learning with Applications, Vol. 16, 100553, 2024. https://doi.org/10.1016/j.mlwa.2024.100553

5. Madan Parajuli, Amy Amara, and Mohamed Shaban, “Deep-Learning Detection of Mild Cognitive Impairment from Sleep Electroencephalography for Patients with Parkinson’s Disease”, PloS One, 2023.  https://doi.org/10.1371/journal.pone.0286506 

6. Kimiko Krieger, Elise Mann, Kevin Lee, Elyse Bolterstein, Deborah Jebakumar, Michael Ittmann, Valeria Dal Zotto, Mohamed Shaban, Arun Sreekumar, and Natalie Gassman, “Spatial mapping of the DNA adducts in Cancer”, DNA Repair, Vol. 128, 2023. https://doi.org/10.1016/j.dnarep.2023.103529

7. Samantha Islam, Akhter Hossain, and Mohamed Shaban, “Older Driver At-Fault Crashes at Unsignalized Intersections in Alabama: Injury Severity Analysis with Supporting Evidence from a Deep Learning Based Approach”, Journal of Safety Research, Vol. 86, pp. 419-428, 2023. https://doi.org/10.1016/j.jsr.2023.04.009

8. Mohamed Shaban, “Deep-Learning for Parkinson’s Disease Diagnosis: A Short Survey”, Computers, Vol. 12, no. 3, 2023. https://doi.org/10.3390/computers12030058

9. Madan Parajuli, Mohamed Shaban, and Thuy Phung, “Automated Differentiation of Skin Melanocytes from Keratinocytes in High-Resolution Histopathology Images Using a Weakly-Supervised Deep-Learning Framework”, International Journal of Imaging Systems and Technology, Vol. 33, no. 1, 2022. https://doi.org/10.1002/ima.22810 

10. Estate Sokhadze, and Mohamed Shaban, “Event-Related Theta and Gamma Oscillations in Cue Reactivity Test in Individuals with Opiate Use Disorder in Buprenorphine-Maintenance Program”, Neuroregulation, Vol. 9, no. 1, pp. 16-28, 2022. https://doi.org/10.15540/nr.9.1.16 

11. Mohamed Shaban, and Amy Amara, “Resting-State Electroencephalography Based Deep-Learning for the Detection of Parkinson’s Disease”, PLoS ONE, 2022. https://doi.org/10.1371/journal.pone.0263159 

12. Mohamed Shaban, “Detection of Early Stage Parkinson’s Disease Using Deep Learning Based Electroencephalography”, Transactions on Techniques in STEM Education, Vol. 7, no. 1, pp. 49-55, 2021. 

13. Manuel Casanova, Mohamed Shaban, Mohammed Ghazal, Ayman El-Baz, Emily Casanova, and Estate Sokhadze, “Ringing Decay of Gamma Oscillations and Transcranial Magnetic Stimulation Therapy in Autism Spectrum Disorder”, Applied Psychophysiology and Biofeedback, pp. 1-13, 2021. https://doi.org/10.1007/s10484-021-09509-z 

14. Mohamed Shaban, Reem Salim, Hadil Abu Khalifeh, Adel Khelifi, Ahmed Shalaby, Shady El-Mashad, Ali Mahmoud, Mohammed Ghazal, and Ayman El-Baz, “A Deep-Learning Framework for the Detection of Oil Spills from SAR Data”, Sensors, Vol. 21, no. 7, 2021.  https://doi.org/10.3390/s21072351 

15. Manuel Casanova, Mohamed Shaban, Mohammed Ghazal, Ayman El-Baz, Emily Casanova, and Estate Sokhadze, “Effects of Transcranial Magnetic Stimulation Therapy on Evoked and Induced Gamma Oscillations in Children with Autism Spectrum Disorder”, Brain Sciences, Vol. 10, no. 7, 2020. http://dx.doi.org/10.3390/brainsci10070423

16. Mohamed Shaban, Zeliha Ogur, Ali Mahmoud, Andrew Switala, Ahmed Shalaby, Hadil Abu Khalifeh, Mohammed Ghazal, Luay Fraiwan, Guruprasad Giridharan, Harpal Sandhu, and Ayman El-Baz, “A Convolutional Neural Network for the Screening and Staging of Diabetic Retinopathy”, PLoS ONE, 2020.  https://doi.org/10.1371/journal.pone.0233514

Peer Reviewed Conference Papers:

1. Mohamed Shaban, “A GRU-LSTM Network Bank for Efficient Detection of Freezing of Gait for Parkinson’s Disease Patients”, Proceedings of the IEEE International Conference on Computing and Machine Intelligence, Mount Pleasant, MI, pp. 1-5, 2025.

2. Md. Bipul Hossain, Na Gong, and Mohamed Shaban, “SACA: Sub-Sampling Aware Channel Attention for Efficient Semantic Segmentation Model Compression”, Proceedings of the IEEE International Symposium on Biomedical Imaging, Houston, TX, pp. 1-5, 2025. 

3. William Oswald, Md. Sajjad Hossain, Kyle Mooney, Mario Renteria Pinon, Md. Bipul Hossain, Mohamed Shaban, Jinhui Wang, and Na Gong, “Flexible Bit-Truncation Memory for Low-Power Quality-Adaptive Video and Deep Learning Storage”, Proceedings of the International Green and Sustainable Computing (IGSC) Conference, Austin, TX, 2024. 

4. Mohamed Shaban, “Prediction of Freezing of Gait for Patients with Parkinson’s Disease Using Deep Learning”, Proceedings of the IEEE International Conference on Artificial Intelligence, Blockchain and Internet of Things, Mount Pleasant, MI, 2024.

5. Md. Bipul Hossain, and Mohamed Shaban, “Resource Efficient Deep Learning Architectures for Histopathology-Based Colorectal Cancer Detection”, Proceedings of the IEEE International Conference on Artificial Intelligence, Blockchain and Internet of Things, Mount Pleasant, MI, 2024. 

6. Md. Bipul Hossain, and Mohamed Shaban, “Low-Complexity Low-Memory VGG Models for Accurate Diagnosis of Breast Cancer”, Proceedings of the IEEE SoutheastCon, Atlanta, GA, 2024. 

7. Md. Bipul Hossain, Na Gong and Mohamed Shaban, “An Improved Lightweight DenseNet-201 Model for Pneumonia Detection on Edge IoT”, Proceedings of the IEEE World Forum on the Internet of Things, Aveiro, Portugal, 2023. 

8. Md. Bipul Hossain, Na Gong, and Mohamed Shaban, “Computational Complexity Reduction Techniques for Deep Neural Networks: A Survey”, Proceedings of IEEE International Conference on Artificial Intelligence, Blockchain and Internet of Things, Mount Pleasant, MI, 2023.

9. Madan Parajuli, Amy Amara, and Mohamed Shaban, “A Novel Deep-Learning Based Approach for Mild Cognitive Impairment Screening in Patients with Parkinson’s Disease”, Proceedings of IEEE International Conference on Artificial Intelligence, Blockchain and Internet of Things, Mount Pleasant, MI, 2023.

10. Madan Parajuli, Amy Amara, and Mohamed Shaban, “Screening of Mild Cognitive Impairment in Patients with Parkinson’s Disease Using a Variational Mode Decomposition Based Deep-Learning”, Proceedings of International IEEE EMBS Conference on Neural Engineering, Baltimore, MD, 2023. 

11. Stephen Cahoon, Mohamed Shaban, Andy Switala, Ali Mahmoud, and Ayman El-Baz, “Diabetic Retinopathy Screening Using a Two-Stage Deep Convolutional Neural Network Trained on An Extremely Un-Balanced Dataset”, IEEE SoutheastCon, Mobile, AL, 2022.

12. Madan Parajuli, Mohamed Shaban, and Thuy Phung, “Efficient Identification of Melanocytic Nuclei in Pathology Images for Melanoma Diagnosis Using A Weakly-Supervised Deep Learning Framework”, IEEE SoutheastCon, Mobile, AL, 2022. 

13. Mohamed Shaban, Stephen Cahoon, Fiza Khan, and Mahalia Polk, “Exploiting the Differential Wavelet Domain of Resting-State EEG Using a Deep-CNN for Screening Parkinson’s Disease”, IEEE Symposium Series on Computational Intelligence, Virtual, 2021.

14. Stephen Cahoon, Fiza Khan, Mahalia Polk, and Mohamed Shaban, “Wavelet-Based Convolutional Neural Network for Parkinson’s Disease Detection in Resting-State Electroencephalography”, IEEE Signal Processing in Medicine and Biology Symposium, Virtual, 2021. 

15. Mohamed Shaban, “Automated Screening of Parkinson’s Disease Using Deep Learning Based Electroencephalography”, International IEEE EMBS Conference on Neural Engineering, Virtual, 2021. 

16. Mohamed Shaban, “Deep Convolutional Neural Network for Parkinson’s Disease Based Handwriting Screening”, IEEE International Symposium on Biomedical Imaging, Iowa City, Iowa, 2020. 

17. Omar Dekhil, Ahmed Naglah, Mohamed Shaban, Ahmed Shalaby, and Ayman El-Baz, “Deep-Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy”, Proceedings of the IEEE International Conference on Imaging Systems & Techniques, pp. 1-4, Abu Dhabi, United Arab Emirates, 2019.

18. Mohamed Shaban, Zeliha Ogur, Ahmed Shalaby, Ali Mahmoud, Mohammed Ghazal, Harpal Sandhu, Henry Kaplan, and Ayman El-Baz, “Automated Staging of Diabetic Retinopathy Using a 2D Convolutional Neural Network”, Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, pp. 354-358, Louisville, Kentucky, 2018.

19. Mohamed Shaban, and Ahmed Abdelgawad, “A Study of Distributed Compressive Sensing for the Internet of Things”, Proceedings of the IEEE World Forum on the Internet of Things, pp. 173-178, Singapore, 2018.

20. Mohamed Shaban, and Magdy Bayoumi, “On Sub-Nyquist Spectrum Sensing for Wideband Cognitive Radios”, Proceedings of the IEEE International Conference on Ubiquitous and Future Networks, pp. 543-548, Vienna, Austria, 2016.

21. Mohamed Shaban, Tarek Idriss, Haytham Idriss, and Magdy Bayoumi, “ASIC Implementation of a Computationally Efficient Compressive Sensing Detection Method Using Least Squares Optimization in 45 nm CMOS Technology”, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1096-1100, Brisbane, Australia, 2015.

22. Mohamed Shaban, Dmitri Perkins, and Magdy Bayoumi, “Application of Compressed Sensing in Wideband Cognitive Radios when Sparsity is Unknown”, Proceedings of the IEEE Wireless and Microwave Technology Conference, pp. 1-4, Tampa, Florida, 2014.

23. Mohamed Shaban, Dmitri Perkins, and Magdy Bayoumi, “An Efficient Compressive Wideband Spectrum Sensing Architecture for Cognitive Radios”, Proceedings of the IEEE International Workshop on Signal Processing Systems, pp. 130-134, Taiwan, 2013.

24. Mohamed Shaban, and Sherif Kishk, “Steering Vector Transformation Technique for the Design of Wideband Beamformer”, Proceedings of the National Radio Science Conference, Egypt, 2010. 

Peer Reviewed Abstracts, and Journal Supplements:

1. Maha Babker, Md. Bipul Hossain, Hector Chavarria Bernal, Mohamed Shaban, and Eric Wei, “From Pixels to Diagnosis: AI Vision Transformer-Based Classification of Breast Epithelial Tumors”, Accepted to Be Presented at the 115th United States and Canadian Academy of Pathology Annual Meeting (USCAP), San Antonio, Texas, 2026.

2. Purpi Adhya, Ruthba Yasmin, Piyas Chowdhury, Mohamed Shaban, and Edmund Spencer, “Substorm Prediction with LSTM: Key Solar Wind Drivers and a Comparative Study of SuperMAG Onset Lists as Reference Labels”, Submitted to The American Geophysical Union (AGU) Fall Meeting, New Orleans, Louisiana, 2025. 

3. Purpi Adhya, Edmund Spencer, Piyas Chowdhury, and Mohamed Shaban, “Substorm Onset Analysis and Prediction Using a Physics-Based Model Combined with Machine Learning Algorithms”, The American Geophysical Union (AGU) Fall Meeting, Washington, District of Colombia, 2024. 

4. Erik Ortiz, Irene Pericot-Valverde, Alain Litwin, Kaileigh Byrne, Ashley Coleman, Mohamed Shaban, and Estate Sokhadze, “Drug cue reactivity and craving correlates in buprenorphine-maintained opioid addicts”, 52nd Association for Applied Psychophysiology and Biofeedback Annual Scientific Meeting, Virtual, 2022. 

5. Estate Sokhadze, Mohamed Shaban, Ayman El-Baz, and Allan Tasman, “Frontal EEG indices of attentional bias and involuntary orienting to pictorial drug-related cues in cocaine addiction”, Neuroregulation Journal, Vol. 8, no. 4, pp. 221-222, 2021. 

6. Estate Sokhadze, Ashley Coleman, Erik Ortiz, Irene Pericot-Valverde, Kaileigh Byrne, Alain Litwin, and Mohamed Shaban, “Cue Reactivity and Craving in Buprenorphine-maintained Opiate-Dependent Patients”, Psychophysiology Journal, Vol. 58, no. S1, page S70, 2021.

7. Erik Ortiz, Ashley Coleman, Irene Pericot-Valverde, Kaileigh Byrne, Alain Litwin, Mohamed Shaban, and Estate Sokhadze, “Pictorial Cue Reactivity and Craving Measures in Individuals with Opiate Use Disorder Enrolled in Buprenorphine-Maintenance Program”, International Journal of Psychophysiology, Elsevier, Vol. 168, Supplement, pp. S137-S138, 2021.

8. Mohamed Shaban, Ayman El-Baz, Mohammed Ghazal, Desmond Kelly, Manuel Casanova, and Estate Sokhadze, “Evoked and Induced Gamma Oscillation Atypicality in Autism Spectrum Disorder”, Psychophysiology Journal, Vol. 57, no. S1, 2020.  

9. Manuel Casanova, Mohamed Shaban, Mohammed Ghazal, Ayman El-Baz, Emily Casanova, and Estate Sokhadze, “Evoked and Induced Gamma Oscillation as Biomarkers of Transcranial Magnetic Stimulation Outcomes in Children with Autism Spectrum Disorder”, Neuroregulation Journal, Vol. 7, no. 4, 177, 2020.  

10. Estate Sokhadze, Mohamed Shaban, Mohammed Ghazal, Ayman El-Baz, and Manuel Casanova, “Neuromodulation of Event-Related Gamma Oscillations in Children with Autism”, Neuroregulation Journal, Vol. 7, no. 4, pp. 180-181, 2020.  

11. Mohamed Shaban, Ali Mahmoud, Andrew Switala, Ahmed Shalaby, Mohammed Ghazal, Luay Fraiwan, Mohamed Khalefa, Guruprasad Giridharan, and Ayman El-Baz, “Robust Categorization of Diabetic Retinopathy Using a Very Deep Convolutional Neural Network”, Biomedical Engineering Society Annual Meeting, San Diego, California, 2020.

12. Mohamed Shaban, Andy Switala, Ahmed Shalaby, Mohammed Ghazal, Harpal Sandhu, Henry Kaplan, and Ayman El-Baz, “Shallow Convolutional Neural Networks for Accurate Staging of Diabetic Retinopathy”, Biomedical Engineering Society Annual Meeting, Philadelphia, Pennsylvania, 2019.

13. Mohamed Shaban, Zeliha Ogur, Ali Aslantas, Ahmed Shalaby, Mohammed Ghazal, Harpal Sandhu, Henry Kaplan, and Ayman El-Baz, “Accurate Diagnosis of Diabetic Retinopathy Using Convolutional Neural Networks”, Biomedical Engineering Society Annual Meeting, Atlanta, Georgia, 2018.