Wandile Nhlapho
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Wandile Nhlapho has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Marcellin Atemkeng
Rhodes University
2 shared publications Jean-Claude Ndogmo
1 shared publication- Jean-Claude Ndogmo
Department of Mathematical and Computational Sciences, University of Venda, P.O. Box 5050, Thohoyandou 0950, South Africa
1 shared publication - Yusuf Brima
Computer Vision, Institute of Cognitive Science, Osnabrück University, Osnabrueck, Germany
1 shared publication
Latest Publications
- Open Access
- 13 September 2025
- Published by Elsevier in Scientific African
- Vol. 30
An attention-guided hybrid statistical and deep learning modeling for enhanced time series forecasting: A case study of South African telecommunication companies
- 13 September 2025
- Published by Elsevier in Scientific African
- Vol. 30
Open AccessAn attention-guided hybrid statistical and deep learning modeling for enhanced time series forecasting: A case study of South African telecommunication companies
- Open Access
- 19 February 2024
- Published by MDPI AG in Preprints.org
Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Classification from MRI Images
The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumor from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making processes, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging state-of-the-art DL frameworks with transfer learning, we conduct a comprehensive evaluation encompassing both classification accuracy and interpretability. Using state-of-the-art DL frameworks with transfer learning, we conduct a thorough evaluation covering both classification accuracy and interpretability. We employ adaptive path-based techniques to understand the underlying decision-making mechanisms of these models. Grad-CAM and Grad-CAM++ highlight critical image regions where the tumors are located.
- 19 February 2024
- Published by MDPI AG in Preprints.org
Open AccessBridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Classification from MRI Images
The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumor from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making processes, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging state-of-the-art DL frameworks with transfer learning, we conduct a comprehensive evaluation encompassing both classification accuracy and interpretability. Using state-of-the-art DL frameworks with transfer learning, we conduct a thorough evaluation covering both classification accuracy and interpretability. We employ adaptive path-based techniques to understand the underlying decision-making mechanisms of these models. Grad-CAM and Grad-CAM++ highlight critical image regions where the tumors are located.