TRANSFER LEARNING APPROACHES FOR IMPROVING MACHINE LEARNING MODEL ON SMALL DATASETS: CASE STUDY SKIN LESION DETECTION
| dc.contributor.author | BENSACI FATEH SAI ABDECHAKOUR | |
| dc.date.accessioned | 2025-04-09T09:06:12Z | |
| dc.date.available | 2025-04-09T09:06:12Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | One of the biggest challenges of working with machine learning is the need for large amounts of data to train accurate models. Small datasets, in particular, can pose significant challenges to machine learning models. Researchers have developed several approaches to address the challenges of working with small datasets, including transfer learning, active learning, and data augmentation. In this study, we employed the transfer learning as a machine learning paradigm that leverages knowledge gained from one task or domain (source domain) to improve performance on a different but related task or domain (target domain). This study focuses on optimizing model accuracy, analyzing feature importance, and understanding the impact of different preprocessing techniques. The results demonstrate significant improvements in diagnostic performance compared to traditional methods, highlighting the potential of deep learning in clinical settings. Future work involves enhancing model generalization and exploring the integration of multimodal data to further refine diagnostic capabilities. We started from VGG16 as a pre-trained model for training a baseline model in a CNN on the domain of skin lesion detection. In the experimental part, we evaluated different structures, including a custom CNN and VGG16, in terms of detection accuracy. The results showed that transfer learning with the pre-trained model VGG16 provided the best performance, achieving an accuracy of 85%, highlighting the importance of transfer learning. | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/8665 | |
| dc.language.iso | en | |
| dc.title | TRANSFER LEARNING APPROACHES FOR IMPROVING MACHINE LEARNING MODEL ON SMALL DATASETS: CASE STUDY SKIN LESION DETECTION | |
| dc.type | Thesis |