Publication: Convolutional neural network-based acne detection using reconstructed hyperspectral images
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Ali, Mohammed Ridha | |
| dc.date.accessioned | 2025-11-27T07:23:47Z | |
| dc.date.available | 2025-11-27T07:23:47Z | |
| dc.date.issued | 2024-01-01 | |
| dc.description.abstract | Acne vulgaris is a common type of skin disease that affects more than 85% of teenagers and frequently continues even in adulthood. Although it is not a dangerous skin disease, it can considerably affect quality of life. With recent advancements in imaging techniques, hyperspectral imaging (HSI), which captures a wide spectrum of light, has emerged as a tool for the detection and diagnosis of various skin conditions. However, its practical use in clinical settings is limited due to the high cost of specialised hyperspectral (HS) cameras. In this research, an acne detection system that utilises reconstructed HS images from red, green, blue (RGB) images is proposed. The study consists of two parts. In the first part, a hierarchical spectral reconstruction algorithm (HRNET) and CNN reconstruction algorithm (HSCNN-D), are proposed to create a dataset of reconstructed acne HS images. The second part of the research focuses on the detection of acne regions by using the RetinaNet algorithm with the ResNet backbone, which is tested by using three datasets, namely, the original HS, reconstructed HS and RGB images. The proposed model with RetinaNet and ResNet produces the best precision (66.14%) when the reconstructed HS image dataset is used and outperforms two existing methods that utilise the Faster R-CNN and F-RCN algorithms. Results suggest that by using a deep-learning approach, the reconstructed HS images have high potential to be used to aid in acne diagnosis. In conclusion, this study has successfully achieved its main objective, which is to develop a reliable RetinaNet-based detection system for acne using reconstructed HS images. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/23232 | |
| dc.language.iso | en | |
| dc.title | Convolutional neural network-based acne detection using reconstructed hyperspectral images | |
| dc.type | Resource Types::text::thesis::master thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |