| No. |
Title |
Details |
| 1 |
Early Diagnosis of Alzheimer’s Disease Using PET Imaging and Deep Learning with Comparative Data Augmentation Techniques |
Muhammad Athar et al., Scientific Reports, 2025. DOI: 10.1038/S41598-025-28866-X |
| 2 |
Binary Classification of Alzheimer’s Disease Using sMRI Imaging Modality and Deep Learning |
Ahsan Bin Tufail et al., Journal of Digital Imaging, Vol. 33, Issue 5, pp. 1073–1090, 2020 |
| 3 |
3D Convolutional Neural Networks-Based Multiclass Classification of Alzheimer’s and Parkinson’s Diseases Using PET and SPECT Neuroimaging Modalities |
Ahsan Bin Tufail et al., Brain Informatics, Vol. 8, Issue 1, pp. 1–9, 2021 |
| 4 |
Classification of Initial Stages of Alzheimer’s Disease Through PET Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches |
Ahsan Bin Tufail et al., Mathematics, Vol. 9, Issue 23, p. 3101, 2021 |
| 5 |
An Efficient Adaptive Modulation Technique Over Realistic Wireless Communication Channels Based on Distance and SINR |
Rahim Khan et al., Frequenz, Vol. 76, Issue 1–2, pp. 83–95, 2022 |
| 6 |
Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains |
Ahsan Bin Tufail et al., Sensors, Vol. 22, Issue 12, p. 4609, 2022 |
| 7 |
On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease |
Ahsan Bin Tufail et al., Sustainability, Vol. 14, p. 14695, 2022 |
| 8 |
Diagnosis of Diabetic Retinopathy Through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples |
Ahsan Bin Tufail et al., Wireless Communications and Mobile Computing, Vol. 2021, 2021 |
| 9 |
Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning |
Rasheed et al., Brain Sciences, Vol. 13, No. 4, p. 602, 2023 |
| 10 |
Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Mini-Review on Challenges, Recent Trends, and Future Directions |
Ahsan Bin Tufail et al., Computational and Mathematical Methods in Medicine, Vol. 2021, 2021 |
| — |
Books / Book Chapters |
— |
| 11 |
Multiclass Classification of Initial Stages of Alzheimer’s Disease Using Structural MRI Phase Images |
Ahsan Bin Tufail et al., IEEE International Conference on Control System, Computing and Engineering, pp. 317–321, 2012 |
| 12 |
Multiclass Classification of Initial Stages of Alzheimer’s Disease Through Neuroimaging Modalities and Convolutional Neural Networks |
Ahsan Bin Tufail et al., IEEE ITOEC, pp. 51–56, 2020 |
| 13 |
Joint Multiclass Classification of Subjects of Alzheimer’s and Parkinson’s Diseases Through Neuroimaging Modalities and Convolutional Neural Networks |
Ahsan Bin Tufail et al., IEEE BIBM, pp. 2840–2846, 2020 |
| 14 |
Binary Classification of Modulation Formats in the Presence of Noise Through Convolutional Neural Networks |
Rahim Khan et al., IEEE ICSP, pp. 386–390, 2020 |
| 15 |
Classification of Subjects of Mild Cognitive Impairment and Alzheimer’s Disease Through Neuroimaging Modalities and Convolutional Neural Networks |
Ahsan Bin Tufail et al., IEEE ICOICT, pp. 1–6, 2020 |
| 16 |
Independent Component Analysis Based Assessment of Linked Gray and White Matter in the Initial Stages of Alzheimer’s Disease Using Structural MRI Phase Images |
Ahsan Bin Tufail et al., IEEE ICSEC, pp. 334–338, 2013 |
| 17 |
Multiclass Classification of Modulation Formats in the Presence of Rayleigh and Rician Channel Noise Using Deep Learning Methods |
Rahim Khan et al., IEEE ICOIACT, pp. 297–301, 2020 |
| 18 |
Assessment of Alzheimer’s Disease Through sMRI Phase Images: A Heuristic Approach Using State-of-the-Art Machine Learning Algorithms |
Ahsan Bin Tufail, LAP Lambert Academic Publishing, 2014 |
