Deep CNN and Data Augmentation for Skin Lesion Classification

2018 ◽  
pp. 573-582 ◽  
Author(s):  
Tri-Cong Pham ◽  
Chi-Mai Luong ◽  
Muriel Visani ◽  
Van-Dung Hoang
Keyword(s):  
Skin Lesion ◽  
Data Augmentation ◽  
Deep Cnn ◽  
Lesion Classification

scholarly journals Skin Lesion Classification With Deep CNN Ensembles

2020 ◽  
Author(s):  
Sara Atito Ali Ahmed ◽  
Berrin Yanikoglu ◽  
Ozgu Goksu ◽  
Erchan Aptoula
Keyword(s):  
Skin Lesion ◽  
Deep Cnn ◽  
Lesion Classification

scholarly journals Skin Lesion Classification Using Additional Patient Information

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qilin Sun ◽  
Chao Huang ◽  
Minjie Chen ◽  
Hui Xu ◽  
Yali Yang
Keyword(s):  
Skin Lesion ◽  
Patient Information ◽  
Data Augmentation ◽  
Challenge Test ◽  
Skin Lesions ◽  
Melanocytic Nevus ◽  
Additional Patient ◽  
Test Time ◽  
Single Model ◽  
Lesion Classification

In this paper, we describe our method for skin lesion classification. The goal is to classify skin lesions based on dermoscopic images to several diagnoses’ classes presented in the HAM (Human Against Machine) dataset: melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), and vascular lesion (VASC). We propose a simplified solution which has a better accuracy than previous methods, but only predicted on a single model that is practical for a real-world scenario. Our results show that using a network with additional metadata as input achieves a better classification performance. This metadata includes both the patient information and the extra information during the data augmentation process. On the international skin imaging collaboration (ISIC) 2018 skin lesion classification challenge test set, our algorithm yields a balanced multiclass accuracy of 88.7% on a single model and 89.5% for the embedding solution, which makes it the currently first ranked algorithm on the live leaderboard. To improve the inference accuracy. Test time augmentation (TTA) is applied. We also demonstrate how Grad-CAM is applied in TTA. Therefore, TTA and Grad-CAM can be integrated in heat map generation, which can be very helpful to assist the clinician for diagnosis.

DermaDL: Advanced Convolutional Neural Networks for Computer-Aided Skin-Lesion Classification

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Daniel M. Lima ◽  
Jose F. Rodrigues-Jr ◽  
Bruno Brandoli ◽  
Lorraine Goeuriot ◽  
Sihem Amer-Yahia
Keyword(s):  
Neural Networks ◽  
Skin Lesion ◽  
Convolutional Neural Networks ◽  
Computer Aided ◽  
Lesion Classification

Wavelet-based deep learning for skin lesion classification

2020 ◽  
Vol 14 (4) ◽  
pp. 720-726 ◽  
Author(s):  
Sertan Serte ◽  
Hasan Demirel
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Lesion Classification

Skin lesion classification using features of 3D border lines

2021 ◽  
Author(s):  
Pedro M. M. Pereira ◽  
Lucas A. Thomaz ◽  
Luis M. N. Tavora ◽  
Pedro A. A. Assuncao ◽  
Rui Fonseca-Pinto ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Lesion Classification
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