Experimental Comparative Study on Autoencoder Performance for Aided Melanoma Skin Disease Recognition | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Article 7, Volume 22, Issue 1, February 2022, Page 88-97 PDF (660.68 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2022.104799.1136 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Zahraa Emad Diame  1; Maryam ElBery 2; Mohammed A.-M. Salem 3; Mohamed Ismail Roushdy 4
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| 1Department of Computer Science, Faculty of Computers and Information Sciences, Ain Shams University Cairo, Egypt | ||||
| 2Ain Shams University - FAculty of Computers | ||||
| 3FCIS _Ain Shams | ||||
| 4Faculty of Computer and Information Technology, Future University in Egypt, Cairo, Egypt | ||||
| Abstract | ||||
| Melanoma is a dangerous and metastatic cancer that may be fatal and it has a high ability to invade other tissues and organs. Early diagnosis is an important reason to recover from melanoma and reduce mortality. So, automatic skin segmentation is considered an enthusiastic study at present. In this paper, we investigate the applicability of deep learning approaches to the segmentation of skin lesions by evaluating five architectures: Deeplabv3plus, Inception-ResNet-v2-unet, mobilenetv2_unet, Resnet50_unet, vgg19_unet by providing a comparative study of those methods. All methods were trained on the ISIC2017 dataset. The methods were trained on the original dataset, and then the dataset was pre-processed for use in training the five methods. We used quantitative evaluation metrics to evaluate the performance of the methods. The Deeplabv3+ architecture showed significant results compared to the rest of the architecture in F1 as high as 89%, Jaccard as high as 83% and Recall as high as 91%. | ||||
| Keywords | ||||
| Deep learning; Segmentation; Skin lesion; Melanoma detection; Lesion segmentation | ||||
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