Stack auto-encoders approach for malignant melanoma diagnosis in dermoscopy images
Arasi, Munya A.; El-Sayed M. El-Horbaty; Salem A.; El-Dahshan, El-sayed;
Abstract
In this paper we present Computer Aided Diagnosis (CAD) system for malignant melanoma diagnostic based on deep learning using Stack Auto-Encoders. The dermoscopy images are taken from Dermatology Information System (DermIS) and DermQuest, the image enhancement is achieved by various pre-processing approaches. The extracted features are based on Discrete Wavelet Transform (DWT) and texture Analysis. These features become the input to Stack Auto-Encoders (SAEs) for training and testing the lesions as malignant or benign. The experimental results show the rate of accuracy for texture analysis and SAEs is 89.3%, while using DWT and SAEs gives a higher rate of accuracy about 94%. The experimental results prove that the proposed approaches are more accurate than other approaches in this field of melanoma diagnosis.
Other data
Title | Stack auto-encoders approach for malignant melanoma diagnosis in dermoscopy images | Authors | Arasi, Munya A.; El-Sayed M. El-Horbaty ; Salem A. ; El-Dahshan, El-sayed | Keywords | Computer Aided Diagnosis;Deep Learning;Feature Extraction;Malignant melanoma;Stack Auto-Encoders | Issue Date | 1-Jul-2017 | Publisher | IEEE | Start page | 403 | End page | 409 | Conference | 2017 IEEE 8th International Conference on Intelligent Computing and Information Systems, ICICIS 2017 | ISBN | 9772371723 | DOI | 10.1109/INTELCIS.2017.8260079 | Scopus ID | 2-s2.0-85047094102 |
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