Skin cancer detection using transfer learning: From system design to mobile deployment
| dc.contributor.author | Patel Kiritbhai, Divyeshkumar | |
| dc.contributor.supervisor | Yang, Hong-Chuan | |
| dc.date.accessioned | 2025-09-17T15:19:59Z | |
| dc.date.available | 2025-09-17T15:19:59Z | |
| dc.date.issued | 2025 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering MEng | |
| dc.description.abstract | Skin cancer is the most common type of cancer. Recognizing the poor availability and reliability of existing solutions for skin cancer detection, we leverage the recent advancements in machine learning models and their on-device capabilities to provide an efficient and handy solution for the early detection of skin cancer. Specifically, we designed a lightweight solution using transfer learning with compact pretrained models to aid in the detection of skin cancer. Our solution can accurately detect malignant skin cancer and identify the five major types of skin cancers from images captured with a handheld dermoscope. We also deploy the solution on an Android mobile device. With our mobile application, general practitioners and remote healthcare workers can make guided referrals to a dermatologist for patients. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/22767 | |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | transfer learning | |
| dc.subject | skin cancer detection | |
| dc.subject | on-device machine learning | |
| dc.subject | mobile application | |
| dc.title | Skin cancer detection using transfer learning: From system design to mobile deployment | |
| dc.type | project |