Skin Lesion Segmentation Using Deep Learning Algorithm and Level Set Method
| dc.contributor.advisor | Menore, Tekeba (Mr.) | |
| dc.contributor.author | Selomie, Kindu | |
| dc.date.accessioned | 2020-03-06T07:08:43Z | |
| dc.date.accessioned | 2023-11-04T15:14:40Z | |
| dc.date.available | 2020-03-06T07:08:43Z | |
| dc.date.available | 2023-11-04T15:14:40Z | |
| dc.date.issued | 2019-06-28 | |
| dc.description.abstract | Skin lesion can be benign or skin cancer. Skin cancer is one of the most dangerous cancers killing so many people all over the world. Skin cancer is the most curable cancer if skin lesions are diagnosed at early stage. Skin lesion segmentation is a crucial phase in automated skin lesion detection towards skin cancer. Segmentation of skin lesion is the most challenging task for dermatologists. The rest phases of computer analysis diagnosis of skin cancer mainly depend on the segmentation result. Due to this, many methods of skin lesion segmentation have been proposed to improve the segmentation technique performance in computer aided diagnosis. In this work, skin lesion segmentation using convolution de-convolution neural network and contour level set method is used to segment dermoscopic skin lesion images. Convolution deconvolution neural network is trained pixel wise for semantic segmentation of pixels into lesion and background. Level set is used to find the exact edges of detected lesion boundary by convolution de-convolution neural network method. In addition to the two main proposed techniques, preprocessing of the input images is applied to remove unwanted artifacts such as hair over the skin lesion image using vector filters and data augmentation to overcome the over fitting problem of proposed deep learning network. 2017 International Skin Imaging Collaboration (ISIC) archive dataset hosted by International Society of Biomedical Imaging (ISBI) for skin lesion analysis towards melanoma detection is used. The performance evaluations on the proposed skin lesion segmentation method is pixel wise average measurements validated against ground truth for test data set are 94.8% intersection over union, 98.80% specificity, 94.84% sensitivity, 97.84% positive predicted value and 95.58% negative predicted value. The proposed method out performs segmentation using convolution deconvolution neural network and level set method by more than 2% and 30% respectively. Therefore, using convolution de-convolution neural network with level set segmentation method of skin lesion results better than convolution de-convolution neural network segmentation and level set segmentation. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/20928 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Dermoscope | en_US |
| dc.subject | Detection | en_US |
| dc.subject | Skin Lesion | en_US |
| dc.subject | Skin Cancer | en_US |
| dc.subject | Segmentation | en_US |
| dc.subject | CDNN | en_US |
| dc.subject | Level Set | en_US |
| dc.title | Skin Lesion Segmentation Using Deep Learning Algorithm and Level Set Method | en_US |
| dc.type | Thesis | en_US |