التفاصيل البيبلوغرافية
| العنوان: |
Hybrid deep learning technique for optimal segmentation and classification of multi-class skin cancer. |
| المؤلفون: |
Subhashini, G.1 (AUTHOR) subhashinigphd@gmail.com, Chandrasekar, A.2 (AUTHOR) |
| المصدر: |
Imaging Science Journal. Dec2024, Vol. 72 Issue 8, p1043-1064. 22p. |
| مصطلحات موضوعية: |
*FEATURE extraction, *SKIN cancer, *TUMOR classification, *DATA reduction, *CANCER diagnosis, *DEEP learning |
| مستخلص: |
This study introduces a novel deep learning-based approach for skin cancer diagnosis and treatment planning to overcome existing limitations. The proposed system employs a series of innovative algorithms, including IQQO for preprocessing, TSSO for cancer region isolation, and FA-MFC for data dimensionality reduction. The USSL-Net DCNN extracts hidden features, and the BGR-QNN enables multi-class classification. Evaluated on Kaggle and ISIC-2019 datasets, the model achieves impressive accuracy, up to 96.458% for Kaggle and 94.238% for ISIC-2019. This hybrid deep learning technique shows great potential for improving skin cancer classification, thus enhancing diagnosis and treatment outcomes and ultimately reducing mortality rates. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
Academic Search Index |