l1 Regularization Based Random Vector Functional Link Network for Alzheimer's Disease Diagnosis Tripti Goel, Raveendra Pilli, Shradha Verma, M Tanveer, R Murugan, P. N. Suganthan Proceedings of the International Joint Conference on Neural Networks, 2025 Alzheimer’s disease (AD) is a neurological condition primarily impacting the elderly and is known for its progressive decline in normal brain functioning. The magnetic resonance imaging (MRI) modality enables disease diagnoses by identifying atrophy patterns and structural changes. Imaging captures anatomical details effectively using axial, sagittal, and coronal planes. The axial plane of MRI provides a cross-sectional view, whereas the coronal plane allows visualization in the anterior-posterior direction. The sagittal plane offers a lateral view and aids in examining asymmetry and bilateral structures of the brain’s anatomy. In this paper, the features of the sagittal plane are extracted using Resnet-50, a deep-learning network. These extracted features are fed to the classifier for AD diagnosis. This paper presents a l1 regularization-based random vector functional link network (RVFL) classifier for AD diagnosis. The optimization problem of the proposed l1 regularization-based RVFL is solved using the Split Bregman iterative method. l1 regularization-based RVFL classifier is more generalizable and produces sparse output. The sparse output indicates a lower number of non-zero elements in the output compared to the standard RVFL network which uses l2 regularization. The experiments are performed on the publicly accessible Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, compared with other feed-forward networks. Results highlight the better performance of the proposed model for AD diagnosis than state-of-the-art approaches.
Weighted Kernel Ridge Regression based Randomized Network for Alzheimer's Disease Diagnosis using Susceptibility Weighted Images M. Tanveer, Shradha Verma, Rahul Sharma, Tripti Goel, P. N. Suganthan Proceedings of the International Joint Conference on Neural Networks, 2023 Alzheimer's disease (AD) is a neurological disorder that primarily affects the elderly and is characterized by cognitive decline and memory loss. Recent research has shown that susceptibility-weighted imaging (SWI) images are useful for diagnosing AD because they reveal abnormally high iron deposition in certain brain regions of people with the disease. Machine learning (ML) algorithms, particularly deep learning (DL) networks, are making incredible strides in AD diagnosis using imaging data to assist physicians in making decisions. The random-vector functional link network (RVFL) is an example of a single-hidden-layer feedforward network that uses a closed-form solution-based approach to offer a variety of feature mapping functions and kernels. In the proposed paper, SWI image features are extracted with a DL network, ResNet 50, and afterward classified with a kernel ridge regression-based RVFL network. To manage data with an unbalanced class distribution, we present a weighted kernel ridge regression-based RVFL network that is capable of generalizing to balanced data. We used SWI images from the publicly accessible OASIS dataset to evaluate the proposed methods for AD diagnosis. Experiment results show that the proposed model outperforms the state-of-the-art models.
Discrete Cosine Transform based Laplacian Phase Unwrapping for Phase Image application Shradha Verma, Tripti Goel, R. Murugan Indicon 2022 2022 IEEE 19th India Council International Conference, 2022 Phase images can be disintegrated from magnetic resonance imaging (MRI) data using the arc tangent function. Since phase contains more details than magnitude image, therefore, exploits in numerous applications, including the medical field [1]. However, the raw phase image acquired has a phase value within the (−π,π] or in the 2π range. This causes an aliasing issue in reconstructing the original phase value. Hence, phase unwrapping (PU) is required to deal with the aforementioned problem. The laplacian phase unwrapping (LPU) technique is popular and easy to implement. For PU, the fast fourier transform (FFT) approach can be employed to accomplish an unwrapped solution of the phase equation in the fourier domain. However, because of the mirroring property and computational time complexity, LPU with FFT takes more computational time. On the contrary, DCT depends only on real values and its computational time complexity of order N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> log <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> N. Therefore, it yields a more accurate phase value and takes less time. The paper proposed DCT for MRI phase image application to avoid the demerits of FFT. Experimental results demonstrate that the aliasing or wrapped effect is mitigated more effectively with DCT than with other techniques.
RECENT SCHOLAR PUBLICATIONS
MRI phase image unwrapping using DCT-based modified weighted least square algorithm S Verma, T Goel Sādhanā 51 (2), 81 , 2026 2026.0 Citations: 2
l1 Regularization Based Random Vector Functional Link Network for Alzheimer’s Disease Diagnosis T Goel, R Pilli, S Verma, M Tanveer, R Murugan, PN Suganthan 2025 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2025 2025.0
Alzheimer’s disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier T Goel, S Verma, M Tanveer, PN Suganthan Information Fusion 118, 102917 , 2025 2025.0 Citations: 6
Alzheimer’s disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier G Tripti, S Verma, M Tanveer, PN Suganthan Elsevier , 2025 2025.0
Quantitative susceptibility mapping in cognitive decline: a review of technical aspects and applications S Verma, T Goel, M Tanveer Cognitive Computation 16 (4), 1992-2008 , 2024 2024.0 Citations: 8
Weighted Kernel Ridge Regression based Randomized Network for Alzheimer's Disease Diagnosis using Susceptibility Weighted Images M Tanveer, S Verma, R Sharma, T Goel, PN Suganthan 2023 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2023 2023.0 Citations: 4
Machine learning techniques for the schizophrenia diagnosis: a comprehensive review and future research directions S Verma, T Goel, M Tanveer, W Ding, R Sharma, R Murugan Journal of Ambient Intelligence and Humanized Computing 14 (5), 4795-4807 , 2023 2023.0 Citations: 50
Discrete cosine transform based laplacian phase unwrapping for phase image application S Verma, T Goel, R Murugan 2022 IEEE 19th India Council International Conference (INDICON), 1-5 , 2022 2022.0 Citations: 3
RVFL classifier based ensemble deep learning for early diagnosis of Alzheimer’s Disease K Maji, R Sharma, S Verma, T Goel International Conference on Neural Information Processing, 616-626 , 2022 2022.0 Citations: 4
OWC IMAGE DE-NOISING FILTER PERFORMANCE COMPARSION USING MATLAB BASED GUI SG SHRADHA VERMA,TARUN THUKRAL,NALINI INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING 6 … , 2018 2018.0
Segmentation of Lung Cancer using Mark Region Growing and Median Filter S Verma International Journal of Computer Applications , 2018 2018.0
Segmentation of Lung Cancer using Mark Region Growing and Median Filter T Thukral, S Verma, S Gaur, N Tyagi International Journal of Computer Applications 975, 8887 , 0 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Machine learning techniques for the schizophrenia diagnosis: a comprehensive review and future research directions S Verma, T Goel, M Tanveer, W Ding, R Sharma, R Murugan Journal of Ambient Intelligence and Humanized Computing 14 (5), 4795-4807 , 2023 2023.0 Citations: 50
Quantitative susceptibility mapping in cognitive decline: a review of technical aspects and applications S Verma, T Goel, M Tanveer Cognitive Computation 16 (4), 1992-2008 , 2024 2024.0 Citations: 8
Alzheimer’s disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier T Goel, S Verma, M Tanveer, PN Suganthan Information Fusion 118, 102917 , 2025 2025.0 Citations: 6
Weighted Kernel Ridge Regression based Randomized Network for Alzheimer's Disease Diagnosis using Susceptibility Weighted Images M Tanveer, S Verma, R Sharma, T Goel, PN Suganthan 2023 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2023 2023.0 Citations: 4
RVFL classifier based ensemble deep learning for early diagnosis of Alzheimer’s Disease K Maji, R Sharma, S Verma, T Goel International Conference on Neural Information Processing, 616-626 , 2022 2022.0 Citations: 4
Discrete cosine transform based laplacian phase unwrapping for phase image application S Verma, T Goel, R Murugan 2022 IEEE 19th India Council International Conference (INDICON), 1-5 , 2022 2022.0 Citations: 3
MRI phase image unwrapping using DCT-based modified weighted least square algorithm S Verma, T Goel Sādhanā 51 (2), 81 , 2026 2026.0 Citations: 2
Segmentation of Lung Cancer using Mark Region Growing and Median Filter T Thukral, S Verma, S Gaur, N Tyagi International Journal of Computer Applications 975, 8887 , 0 Citations: 1
l1 Regularization Based Random Vector Functional Link Network for Alzheimer’s Disease Diagnosis T Goel, R Pilli, S Verma, M Tanveer, R Murugan, PN Suganthan 2025 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2025 2025.0
Alzheimer’s disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier G Tripti, S Verma, M Tanveer, PN Suganthan Elsevier , 2025 2025.0
OWC IMAGE DE-NOISING FILTER PERFORMANCE COMPARSION USING MATLAB BASED GUI SG SHRADHA VERMA,TARUN THUKRAL,NALINI INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING 6 … , 2018 2018.0
Segmentation of Lung Cancer using Mark Region Growing and Median Filter S Verma International Journal of Computer Applications , 2018 2018.0
