TLR-Net :Transfer Learning in Residual U-Net for Enhancing Skin Lesion Segmentation

Author:

R P Aneesh1ORCID,Zacharias Joseph1ORCID

Affiliation:

1. Electronics and Communication, College of Engineering Trivandrum, India

Publisher

ACM

Reference37 articles.

1. Ashi Agarwal, Ashish Issac, and Malay Kishore Dutta. 2017. A region growing based imaging method for lesion segmentation from dermoscopic images. In 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON). IEEE, 632–637.

2. Monitoring of pigmented skin lesions using 3D whole body imaging;Ahmedt-Aristizabal David;Computer Methods and Programs in Biomedicine,2023

3. Mohammed A Al-Masni, Mugahed A Al-Antari, Mun-Taek Choi, Seung-Moo Han, and Tae-Seong Kim. 2018. Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Computer methods and programs in biomedicine 162 (2018), 221–231.

4. Skin lesion segmentation from dermoscopic images by using Mask R-CNN, Retina-Deeplab, and graph-based methods;Bagheri Fatemeh;Biomedical Signal Processing and Control,2021

5. Lei Bi, Dagan Feng, and Jinman Kim. 2018. Improving automatic skin lesion segmentation using adversarial learning based data augmentation. arXiv preprint arXiv:1807.08392 (2018).

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