Accès libre

Deep Learning Based Melanoma Diagnosis Identification

Gaole Duan

Gaole Duan

School of Computer Science and Engineering, Xi'an Technological UniversityXi’an, China
Recherchez cet auteur sur
Sciendo|Google Scholar
Duan, Gaole
 et 
Changyuan Wang

Changyuan Wang

School of Computer Science and Engineering, Xi'an Technological UniversityXi’an, China
Recherchez cet auteur sur
Sciendo|Google Scholar
Wang, Changyuan
  
16 août 2023
Deep Learning Based Melanoma Diagnosis Identification's Cover Image
À propos de cet article
Article précédent
Article suivant
Citez
Télécharger la couverture
Publié en ligne: 16 août 2023
Pages: 20 - 26
DOI: https://doi.org/10.2478/ijanmc-2023-0053
Mots clés
© 2023 Gaole Duan et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Image showing comparison between bengin and maligant skin lesion
Image showing comparison between bengin and maligant skin lesion

Figure 2.

The Relu function in two dimensions
The Relu function in two dimensions

Figure 3.

Mapping relationship between 3*3 convolution kernel and 5*5 convolution kernel
Mapping relationship between 3*3 convolution kernel and 5*5 convolution kernel

Figure 4.

Overall network structure of VGG16
Overall network structure of VGG16

Figure 5.

Training Accuracy Curves of the proposed method on the ISIC-2016 datasets
Training Accuracy Curves of the proposed method on the ISIC-2016 datasets

Figure 6.

Training Loss Curves of the proposed method on the ISIC-2016 datasets
Training Loss Curves of the proposed method on the ISIC-2016 datasets

Figure 7.

Training Eval_Top1 Curves of the proposed method on the ISIC-2016 datasets
Training Eval_Top1 Curves of the proposed method on the ISIC-2016 datasets

Figure 8.

Training Eval_Top5 Curves of the proposed method on the ISIC-2016 datasets
Training Eval_Top5 Curves of the proposed method on the ISIC-2016 datasets

Figure 9.

Prediction results of the proposed method for malignant melanoma in this study
Prediction results of the proposed method for malignant melanoma in this study

Figure 10.

Prediction results of the proposed method for benign melanoma in this study
Prediction results of the proposed method for benign melanoma in this study

Feasibility of 3*3 Convolution Kernels Replace 5*5 Convolution Kernels

Assuming: feature_map = 28*28 Convolution step = 1 Padding = 0
1-Layer 5×5 convolutional kernel 2-Layer 3×3 convolutional kernel
Layer1: (28-5) / 1 + 1 = 24 Layer1:(28-3) / 1 + 1 = 26
Output: Feature map = 24×24 Layer2:(26-3) / 1 + 1 = 24
Output:Feature map = 24×24

Confusion matrix under binary classification

Confusion Matrix Predict
0 1
Real 0 a b
1 c d
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Informatique, Informatique, autres
RSS Feed de la revue