Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
Abstract
:1. Introduction
2. Related Studies
- The introduction of two majority voting ensemble deep learning algorithms to accurately classify up to ten different skin lesion classes with improved classification performance.
- The application of distributed computing paradigm to achieve a faster and more timely multiclass classification of skin lesions into one of three or multiple classes.
- The comprehensive evaluation of the introduced majority voting ensemble deep learning algorithms against 28 state-of-the-art deep learning and ensemble learning algorithms as a way of demonstrating their prowess.
3. Materials and Methods
3.1. Dataset
3.2. Methods
3.2.1. Performance Evaluation
3.2.2. Experimental Setup
3.3. Proposed Ensemble Deep Learning Algorithm
3.3.1. Simple Majority Voting Ensemble Deep Learning Algorithm
Algorithm 1: Simple Majority Voting Ensemble Deep Learning |
Given an input image į 1. Broadcast į to the respective handlers (Ϧ1, Ϧ2, …Ϧn) of the learners. 2. Compute the prediction for each handler using distributed processing [80]. 3. Compile responses from all handlers of the learners. 4. Aggregate the results of the handlers based on the maximum voting principle. 5. Determine the class prediction P(ҡj) using Equation (2). End |
3.3.2. Weighted Majority Voting Ensemble Deep Learning Algorithm
Algorithm 2: Weighted Majority Voting Ensemble Deep Learning |
Given an input image į 1. Broadcast į to the respective handlers (Ϧ1, Ϧ2, …Ϧn) of the learners. 2. Compute the prediction for each handler using distributed processing [80]. 3. Compile responses from all handlers of the learners. 4. Compute ѿj for each ᴟj. 5. Aggregate the results of the handlers with ѿj >= 0.25. 6. If exactly one class ҡj has the highest predicted output ᴟj P(ҡj) = ҡj Else P(ҡj) = ҡj with the maximum average weighted confidence μѿj End |
4. Experimental Results
4.1. Regularization and Optimization Outcomes
4.2. Stage 1 Evaluation
4.3. Stage 2 Evaluation
4.4. Comparison with State-of-the-Art Algorithms
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Datasets | Descriptions | Sizes | Classes |
---|---|---|---|
ISIC_2017_TRN [4] | Training set for ISIC 2017 Classification Task | 2000 | 3 |
ISIC_2017_VAL [4] | The validation set for ISIC 2017 Classification Task | 150 | 3 |
ISIC_2016_TRN [66] | Training set for ISIC 2016 Classification Task | 900 | 2 |
ISIC_2019_TRN [4,67,69] | Training set for ISIC 2019 Classification Task | 25,331 | 9 |
ISIC_2018_TRN [67,68] | Training set for ISIC 2018 Classification Task | 10,015 | 7 |
ISIC_2020_TRN [70] | Training set for ISIC 2020 Classification Task | 33,126 | 9 |
Database | Benign | Malignant | Total | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
AKIEC | BKL | DF | INDB | NV | SK | VASC | BCC | MEL | SCC | ||
ISIC_2017_TRN [4] | 1372 | 254 | 374 | 2000 | |||||||
ISIC_2017_VAL [4] | 78 | 42 | 30 | 150 | |||||||
ISIC_2016_TRN [66] | 1 | 29 | 688 | 182 | 900 | ||||||
ISIC_2019_TRN [4,67,69] | 867 | 2624 | 239 | 12,875 | 253 | 3323 | 4522 | 628 | 25,331 | ||
ISIC_2018_TRN [67,68] | 327 | 1099 | 115 | 6705 | 142 | 514 | 1113 | 10,015 | |||
ISIC_2020_TRN [70] | 27,124 | 5193 | 135 | 584 | 33,036 | ||||||
Total | 1194 | 3724 | 354 | 27,153 | 26,911 | 431 | 395 | 3837 | 6805 | 628 | |
Duplicates | 327 | 1396 | 115 | 8843 | 142 | 514 | 1687 | ||||
Discarded | 29 | 12 | |||||||||
Final Selection | 867 | 2328 | 239 | 27,124 | 18,068 | 431 | 253 | 3323 | 5106 | 628 |
Metrics | Symbols | Formulae |
---|---|---|
Sensitivity | Sn | |
Specificity | Sp | |
Accuracy | Acc | |
Jaccard index | Ji | |
Dice coefficient | DSC | |
Multiclass accuracy | MAcc | |
Precision | Pr | |
Matthew correlation coefficient | MCC |
Learning Algorithm | Weight Decay | Learning Rate | Batch Size | Drop Out a |
---|---|---|---|---|
VGG (11, 16) [56] | 0.0100 | 0.0010 | 12 | 0.2500 to 0.5000 |
VGG-13 [56] | 0.0100 | 0.0006 | 24 | 0.1250 |
VGG-19 [56] | 0.0100 | 0.0007 | 24 | 0.1250 |
ResNet (18, 34, 50) [57] | 0.0100 | 0.0010 | 12 | 0.2500 to 0.5000 |
ResNet (101 and 152) [57] | 0.0100 | 0.0200 | 24 | 0.2500 to 0.5000 |
ResNeXt-50 [58] | 0.0010 | 0.0020 | 24 | 0.2500 to 0.5000 |
ResNeXt-101 [58] | 0.0100 | 0.0010 | 8 | 0.2500 to 0.5000 |
DenseNet (121, 169, 201) [59] | 0.0100 | 0.0020 | 24 | 0.250 to 0.5000 |
DenseNet-161) [59] | 0.0100 | 0.0010 | 20 | 0.250 to 0.5000 |
DPN (68, 92) [60] | 0.0100 | 0.0010 | 12 | 0.250 to 0.5000 |
DPN-98 [60] | 0.0100 | 0.0004 | 32 | 0.1250 |
DPN-107 [60] | 0.0100 | 0.0008 | 32 | 0.1250 |
DPN-131 [60] | 0.0100 | 0.0006 | 32 | 0.1250 |
ECA-ResNet-101 [61] | 0.0100 | 0.0025 b, 0.0017 c | 32 | 0.1250 |
IG-ResNeXt-101 [62] | 0.0100 | 0.0044 b, 0.0007 c | 24 d, 32 e | 0.1250 |
SWSL-ResNeXt-101 [63] | 0.0100 | 0.0001 b, 0.0006 c | 32 | 0.1250 |
Inception-ResNet-v2 [64] | 0.0100 | 0.0036 | 32 | 0.1250 |
ReXNet_1.5 [65] | 0.0100 | 0.0020 | 24 | 0.1250 |
ReXNet_2.0 [65] | 0.0100 | 0.0010 | 32 | 0.1250 |
Learning Algorithm | Segmented | Sn% | Sp% | Acc% | Ji | DSC |
---|---|---|---|---|---|---|
VGG-11 [56] | ✓ | 70.63 | 99.38 | 90.63 | 0.70 | 0.82 |
VGG-11 [56] | x | 75.00 | 98.75 | 90.31 | 0.74 | 0.85 |
VGG-13 [56] | ✓ | 63.75 | 98.75 | 92.81 | 0.63 | 0.77 |
VGG-13 [56] | x | 88.75 | 98.13 | 95.31 | 0.87 | 0.93 |
VGG-16 [56] | ✓ | 71.25 | 98.13 | 91.25 | 0.70 | 0.82 |
VGG-16 [56] | x | 66.25 | 99.38 | 93.44 | 0.66 | 0.79 |
VGG-19 [56] | ✓ | 78.75 | 98.13 | 94.06 | 0.77 | 0.87 |
VGG-19 [56] | x | 89.38 | 100.00 | 98.44 | 0.89 | 0.94 |
ResNet-18 [57] | ✓ | 44.38 | 99.38 | 93.12 | 0.44 | 0.61 |
ResNet-18 [57] | x | 75.63 | 100.00 | 89.06 | 0.76 | 0.86 |
ResNet-34 [57] | ✓ | 78.75 | 98.75 | 97.50 | 0.78 | 0.88 |
ResNet-34 [57] | x | 50.00 | 100.00 | 90.94 | 0.50 | 0.67 |
ResNet-50 [57] | ✓ | 78.75 | 98.13 | 90.63 | 0.77 | 0.87 |
ResNet-50 [57] | x | 65.63 | 100.00 | 91.56 | 0.66 | 0.79 |
ResNet-101 [57] | ✓ | 50.00 | 99.38 | 94.69 | 0.50 | 0.66 |
ResNet-101 [57] | x | 90.00 | 97.50 | 93.75 | 0.88 | 0.94 |
ResNet-152 [57] | ✓ | 68.75 | 99.38 | 91.56 | 0.68 | 0.81 |
ResNet-152 [57] | x | 89.38 | 99.38 | 94.38 | 0.89 | 0.94 |
ResNeXt-50 [58] | ✓ | 35.63 | 100.00 | 85.63 | 0.36 | 0.53 |
ResNeXt-50 [58] | x | 89.38 | 99.38 | 94.38 | 0.89 | 0.94 |
ResNeXt-101 [58] | ✓ | 80.00 | 99.38 | 95.00 | 0.80 | 0.89 |
ResNeXt-101 [58] | x | 85.00 | 99.38 | 95.00 | 0.84 | 0.92 |
DenseNet-121 [59] | ✓ | 80.00 | 99.38 | 89.69 | 0.80 | 0.89 |
DenseNet-121 [59] | x | 88.75 | 100.00 | 94.38 | 0.89 | 0.94 |
DenseNet-161 [59] | ✓ | 85.00 | 99.38 | 92.19 | 0.84 | 0.92 |
DenseNet-161 [59] | x | 91.88 | 100.00 | 95.94 | 0.92 | 0.96 |
DenseNet-169 [59] | ✓ | 70.63 | 99.38 | 85.00 | 0.70 | 0.82 |
DenseNet-169 [59] | x | 88.75 | 100.00 | 94.38 | 00.89 | 0.94 |
DenseNet-201 [59] | ✓ | 79.38 | 100.00 | 89.69 | 0.079 | 0.89 |
DenseNet-201 [59] | x | 90.63 | 99.38 | 95.00 | 0.90 | 0.95 |
DPN-68 [60] | ✓ | 69.38 | 100.00 | 84.69 | 0.69 | 0.82 |
DPN-68 [60] | x | 77.50 | 99.38 | 88.44 | 0.77 | 0.87 |
DPN-92 [60] | ✓ | 66.88 | 99.38 | 83.13 | 0.66 | 0.80 |
DPN-92 [60] | x | 70.72 | 99.38 | 84.16 | 0.70 | 0.83 |
DPN-98 [60] | ✓ | 76.25 | 98.75 | 87.50 | 0.75 | 0.86 |
DPN-98 [60] | x | 91.25 | 100.00 | 95.63 | 0.91 | 0.95 |
DPN-107 [60] | ✓ | 86.88 | 99.38 | 93.13 | 0.86 | 0.93 |
DPN-107 [60] | x | 84.38 | 99.38 | 91.88 | 0.84 | 0.91 |
DPN-131 [60] | ✓ | 72.50 | 99.38 | 85.94 | 0.72 | 0.84 |
DPN-131 [60] | x | 88.13 | 100.00 | 94.06 | 0.88 | 0.94 |
ECA-ResNet-101 [61] | ✓ | 48.75 | 100.00 | 74.38 | 0.49 | 0.66 |
ECA-ResNet-101 [61] | x | 86.88 | 100.00 | 93.44 | 0.87 | 0.93 |
IG-ResNeXt-101 [61] | ✓ | 76.25 | 98.13 | 87.19 | 0.75 | 0.86 |
IG-ResNeXt-101 [61] | x | 89.38 | 98.75 | 94.06 | 0.88 | 0.94 |
SWSL-ResNeXt-101 [63] | ✓ | 80.63 | 98.75 | 89.69 | 0.80 | 0.89 |
SWSL-ResNeXt-101 [63] | x | 85.00 | 100.00 | 92.50 | 0.85 | 0.92 |
Inception-ResNet-v2 [64] | ✓ | 75.63 | 95.63 | 85.63 | 0.72 | 0.84 |
Inception-ResNet-v2 [64] | x | 85.63 | 99.38 | 92.50 | 0.85 | 0.92 |
ReXNet_1.5 [65] | ✓ | 80.63 | 98.75 | 89.69 | 0.80 | 0.89 |
ReXNet_1.5 [65] | x | 88.75 | 100.00 | 94.38 | 0.89 | 0.94 |
ReXNet_2.0 [65] | ✓ | 51.25 | 98.75 | 75.00 | 0.51 | 0.67 |
ReXNet_2.0 [65] | x | 53.13 | 99.38 | 76.25 | 0.53 | 0.69 |
Learning Algorithm | Class | Sn% | Sp% | Acc% | MAcc% | Pr% | Ji | DSC | MCC | Weight |
---|---|---|---|---|---|---|---|---|---|---|
VGG-13 [56] | AKIEC | 49.43 | 99.43 | 98.67 | 86.53 | 56.86 | 0.36 | 0.53 | 0.52 | 0.87 |
BCC | 70.89 | 99.26 | 97.70 | 84.67 | 0.63 | 0.77 | 0.76 | |||
BKL | 69.40 | 97.68 | 96.50 | 56.62 | 0.45 | 0.62 | 0.61 | |||
DF | 59.57 | 99.92 | 99.76 | 75.68 | 0.50 | 0.67 | 0.67 | |||
MEL | 60.50 | 97.56 | 94.39 | 69.94 | 0.48 | 0.65 | 0.62 | |||
NV | 88.65 | 93.81 | 92.22 | 86.44 | 0.78 | 0.88 | 0.82 | |||
SCC | 55.74 | 99.52 | 99.06 | 54.84 | 0.38 | 0.55 | 0.55 | |||
SK | 47.06 | 99.55 | 99.17 | 43.48 | 0.29 | 0.45 | 0.45 | |||
VASC | 88.00 | 99.91 | 99.86 | 81.48 | 0.73 | 0.85 | 0.85 | |||
INDB | 95.96 | 95.52 | 95.73 | 94.97 | 0.91 | 0.95 | 0.91 | |||
VGG-19 [56] | AKIEC | 51.14 | 99.69 | 98.95 | 88.24 | 71.43 | 0.42 | 0.60 | 0.60 | 0.88 |
BCC | 75.43 | 99.38 | 98.07 | 87.64 | 0.68 | 0.81 | 0.80 | |||
BKL | 70.02 | 98.36 | 97.18 | 65.08 | 0.51 | 0.67 | 0.66 | |||
DF | 63.83 | 99.91 | 99.77 | 75.00 | 0.53 | 0.69 | 0.69 | |||
MEL | 63.30 | 97.65 | 94.71 | 71.61 | 0.51 | 0.67 | 0.64 | |||
NV | 90.46 | 94.54 | 93.28 | 88.06 | 0.81 | 0.89 | 0.84 | |||
SCC | 65.57 | 99.33 | 98.98 | 50.96 | 0.40 | 0.57 | 0.57 | |||
SK | 48.24 | 99.59 | 99.22 | 46.59 | 0.31 | 0.47 | 0.47 | |||
VASC | 84.00 | 99.96 | 99.89 | 89.36 | 0.76 | 0.87 | 0.87 | |||
INDB | 97.02 | 95.89 | 96.42 | 95.42 | 0.93 | 0.96 | 0.93 | |||
ResNet-101 [57] | AKIEC | 60.80 | 99.69 | 99.10 | 90.42 | 74.83 | 0.50 | 0.67 | 0.67 | 0.90 |
BCC | 85.29 | 99.42 | 98.65 | 89.49 | 0.78 | 0.87 | 0.87 | |||
BKL | 79.06 | 98.47 | 97.66 | 69.24 | 0.59 | 0.74 | 0.73 | |||
DF | 76.60 | 99.97 | 99.87 | 90.00 | 0.71 | 0.83 | 0.83 | |||
MEL | 65.30 | 98.44 | 95.61 | 79.73 | 0.56 | 0.72 | 0.70 | |||
NV | 92.24 | 95.32 | 94.37 | 89.76 | 0.83 | 0.91 | 0.87 | |||
SCC | 63.93 | 99.74 | 99.37 | 72.22 | 0.51 | 0.68 | 0.68 | |||
SK | 41.18 | 99.84 | 99.41 | 64.81 | 0.34 | 0.50 | 0.51 | |||
VASC | 92.00 | 99.98 | 99.95 | 95.83 | 0.88 | 0.94 | 0.94 | |||
INDB | 97.84 | 96.00 | 96.86 | 95.57 | 0.94 | 0.97 | 0.94 | |||
ResNet-152 [57] | AKIEC | 62.50 | 99.75 | 99.19 | 90.29 | 79.14 | 0.54 | 0.70 | 0.70 | 0.90 |
BCC | 86.23 | 99.33 | 98.61 | 88.16 | 0.77 | 0.87 | 0.86 | |||
BKL | 76.18 | 99.01 | 98.06 | 76.97 | 0.62 | 0.77 | 0.76 | |||
DF | 85.11 | 99.91 | 99.85 | 80.00 | 0.70 | 0.82 | 0.82 | |||
MEL | 67.10 | 98.56 | 95.86 | 81.33 | 0.58 | 0.74 | 0.72 | |||
NV | 91.40 | 95.85 | 94.48 | 90.75 | 0.84 | 0.91 | 0.87 | |||
SCC | 71.31 | 99.76 | 99.46 | 75.65 | 0.58 | 0.73 | 0.73 | |||
SK | 45.88 | 99.82 | 99.43 | 65.00 | 0.37 | 0.54 | 0.54 | |||
VASC | 94.00 | 99.98 | 99.96 | 95.92 | 0.90 | 0.95 | 0.95 | |||
INDB | 97.53 | 94.03 | 95.67 | 93.52 | 0.91 | 0.95 | 0.91 | |||
ResNeXt-50 [58] | AKIEC | 64.77 | 99.75 | 99.22 | 90.35 | 79.72 | 0.56 | 0.71 | 0.71 | 0.90 |
BCC | 85.76 | 99.43 | 98.68 | 89.69 | 0.78 | 0.88 | 0.87 | |||
BKL | 76.18 | 98.78 | 97.83 | 73.03 | 0.59 | 0.75 | 0.73 | |||
DF | 85.11 | 99.90 | 99.84 | 76.92 | 0.68 | 0.81 | 0.81 | |||
MEL | 65.30 | 98.73 | 95.86 | 82.76 | 0.57 | 0.73 | 0.71 | |||
NV | 92.43 | 95.19 | 94.34 | 89.52 | 0.83 | 0.91 | 0.87 | |||
SCC | 74.59 | 99.74 | 99.48 | 75.21 | 0.60 | 0.75 | 0.75 | |||
SK | 40.00 | 99.82 | 99.38 | 61.82 | 0.32 | 0.49 | 0.49 | |||
VASC | 92.00 | 99.97 | 99.93 | 92.00 | 0.85 | 0.92 | 0.92 | |||
INDB | 97.72 | 95.40 | 96.49 | 94.94 | 0.93 | 0.96 | 0.93 | |||
DenseNet-121 [59] | AKIEC | 28.41 | 99.62 | 98.54 | 86.72 | 53.19 | 0.23 | 0.37 | 0.38 | 0.87 |
BCC | 83.41 | 97.61 | 96.83 | 66.88 | 0.59 | 0.74 | 0.73 | |||
BKL | 59.55 | 98.06 | 96.45 | 57.20 | 0.41 | 0.58 | 0.57 | |||
DF | 10.64 | 99.97 | 99.61 | 62.50 | 0.10 | 0.18 | 0.26 | |||
MEL | 58.80 | 98.07 | 94.71 | 74.06 | 0.49 | 0.66 | 0.63 | |||
NV | 89.84 | 94.75 | 93.24 | 88.39 | 0.80 | 0.89 | 0.84 | |||
SCC | 12.30 | 99.85 | 98.94 | 46.88 | 0.11 | 0.19 | 0.24 | |||
SK | 0.00 | 99.92 | 99.19 | 0.00 | 0.00 | 0.00 | 0.00 | |||
VASC | 52.00 | 99.73 | 99.53 | 45.61 | 0.32 | 0.49 | 0.48 | |||
INDB | 98.43 | 94.60 | 96.39 | 94.15 | 0.93 | 0.96 | 0.94 | |||
DenseNet-161 [59] | AKIEC | 13.07 | 99.70 | 98.40 | 80.21 | 40.35 | 0.11 | 0.20 | 0.22 | 0.80 |
BCC | 80.13 | 95.27 | 94.44 | 49.52 | 0.44 | 0.61 | 0.60 | |||
BKL | 30.60 | 98.52 | 95.69 | 47.45 | 0.23 | 0.37 | 0.36 | |||
DF | 0.00 | 99.05 | 98.66 | 0.00 | 0.00 | 0.00 | 0.00 | |||
MEL | 35.50 | 98.22 | 92.85 | 65.14 | 0.30 | 0.46 | 0.45 | |||
NV | 87.65 | 90.36 | 89.52 | 80.17 | 0.72 | 0.84 | 0.76 | |||
SCC | 0.00 | 100.00 | 98.95 | 0.00 | 0.00 | 0.00 | 0.00 | |||
SK | 0.00 | 100.00 | 99.27 | 0.00 | 0.00 | 0.00 | 0.00 | |||
VASC | 12.00 | 99.43 | 99.06 | 8.33 | 0.05 | 0.10 | 0.10 | |||
INDB | 94.43 | 92.84 | 93.58 | 92.09 | 0.87 | 0.93 | 0.87 | |||
DenseNet-169 [59] | AKIEC | 51.14 | 99.65 | 98.92 | 89.30 | 69.23 | 0.42 | 0.59 | 0.59 | 0.89 |
BCC | 79.34 | 99.44 | 98.34 | 89.10 | 0.72 | 0.84 | 0.83 | |||
BKL | 75.56 | 98.19 | 97.25 | 64.56 | 0.53 | 0.70 | 0.68 | |||
DF | 80.85 | 99.89 | 99.81 | 74.51 | 0.63 | 0.78 | 0.78 | |||
MEL | 66.70 | 97.80 | 95.13 | 73.95 | 0.54 | 0.70 | 0.68 | |||
NV | 90.46 | 95.46 | 93.92 | 89.86 | 0.82 | 0.90 | 0.86 | |||
SCC | 52.46 | 99.75 | 99.25 | 68.82 | 0.42 | 0.60 | 0.60 | |||
SK | 45.88 | 99.79 | 99.40 | 61.90 | 0.36 | 0.53 | 0.53 | |||
VASC | 94.00 | 99.93 | 99.91 | 85.45 | 0.81 | 0.90 | 0.90 | |||
INDB | 97.81 | 95.66 | 96.67 | 95.22 | 0.93 | 0.96 | 0.93 | |||
DenseNet-201 [59] | AKIEC | 66.48 | 99.70 | 99.20 | 90.28 | 77.48 | 0.56 | 0.72 | 0.71 | 0.90 |
BCC | 86.23 | 99.45 | 98.72 | 90.03 | 0.79 | 0.88 | 0.87 | |||
BKL | 77.82 | 98.69 | 97.82 | 72.19 | 0.60 | 0.75 | 0.74 | |||
DF | 85.11 | 99.97 | 99.91 | 93.02 | 0.80 | 0.89 | 0.89 | |||
MEL | 69.80 | 98.14 | 95.71 | 77.81 | 0.58 | 0.74 | 0.71 | |||
NV | 91.40 | 95.78 | 94.43 | 90.60 | 0.83 | 0.91 | 0.87 | |||
SCC | 72.13 | 99.85 | 99.56 | 83.81 | 0.63 | 0.78 | 0.78 | |||
SK | 43.53 | 99.79 | 99.38 | 60.66 | 0.34 | 0.51 | 0.51 | |||
VASC | 88.00 | 99.98 | 99.93 | 95.65 | 0.85 | 0.92 | 0.92 | |||
INDB | 97.97 | 96.05 | 96.95 | 95.63 | 0.94 | 0.97 | 0.94 | |||
DPN-98 [60] | AKIEC | 59.09 | 99.70 | 99.09 | 90.36 | 75.36 | 0.50 | 0.66 | 0.66 | 0.90 |
BCC | 85.76 | 99.47 | 98.71 | 90.28 | 0.79 | 0.88 | 0.87 | |||
BKL | 77.21 | 98.76 | 97.86 | 73.01 | 0.60 | 0.75 | 0.74 | |||
DF | 74.47 | 99.93 | 99.83 | 81.40 | 0.64 | 0.78 | 0.78 | |||
MEL | 69.60 | 98.31 | 95.85 | 79.45 | 0.59 | 0.74 | 0.72 | |||
NV | 91.51 | 95.67 | 94.39 | 90.38 | 0.83 | 0.91 | 0.87 | |||
SCC | 69.67 | 99.68 | 99.37 | 69.67 | 0.53 | 0.70 | 0.69 | |||
SK | 32.94 | 99.82 | 99.33 | 57.14 | 0.26 | 0.42 | 0.43 | |||
VASC | 94.00 | 99.94 | 99.91 | 87.04 | 0.82 | 0.90 | 0.90 | |||
INDB | 97.57 | 95.32 | 96.38 | 94.85 | 0.93 | 0.96 | 0.93 | |||
DPN-131 [60] | AKIEC | 59.66 | 99.74 | 99.13 | 90.60 | 77.78 | 0.51 | 0.68 | 0.68 | 0.91 |
BCC | 87.17 | 99.42 | 98.75 | 89.69 | 0.79 | 0.88 | 0.88 | |||
BKL | 78.03 | 98.91 | 98.04 | 75.70 | 0.62 | 0.77 | 0.76 | |||
DF | 85.11 | 99.95 | 99.89 | 86.96 | 0.75 | 0.86 | 0.86 | |||
MEL | 69.70 | 98.33 | 95.88 | 79.66 | 0.59 | 0.74 | 0.72 | |||
NV | 92.46 | 95.32 | 94.44 | 89.79 | 0.84 | 0.91 | 0.87 | |||
SCC | 75.41 | 99.72 | 99.47 | 74.19 | 0.60 | 0.75 | 0.75 | |||
SK | 38.82 | 99.78 | 99.34 | 56.90 | 0.30 | 0.46 | 0.47 | |||
VASC | 90.00 | 99.95 | 99.91 | 88.24 | 0.80 | 0.89 | 0.89 | |||
INDB | 96.91 | 95.87 | 96.36 | 95.40 | 0.93 | 0.96 | 0.93 | |||
ECA-ResNet-101 [61] | AKIEC | 64.20 | 99.68 | 99.14 | 90.50 | 75.33 | 0.53 | 0.69 | 0.69 | 0.90 |
BCC | 84.19 | 99.52 | 98.68 | 91.03 | 0.78 | 0.87 | 0.87 | |||
BKL | 78.85 | 98.74 | 97.91 | 73.14 | 0.61 | 0.76 | 0.75 | |||
DF | 80.85 | 99.93 | 99.85 | 82.61 | 0.69 | 0.82 | 0.82 | |||
MEL | 72.20 | 97.88 | 95.68 | 76.16 | 0.59 | 0.74 | 0.72 | |||
NV | 90.82 | 96.26 | 94.59 | 91.53 | 0.84 | 0.91 | 0.87 | |||
SCC | 67.21 | 99.77 | 99.43 | 75.23 | 0.55 | 0.71 | 0.71 | |||
SK | 40.00 | 99.78 | 99.35 | 57.63 | 0.31 | 0.47 | 0.48 | |||
VASC | 96.00 | 99.96 | 99.94 | 90.57 | 0.87 | 0.93 | 0.93 | |||
INDB | 97.57 | 95.39 | 96.41 | 94.92 | 0.93 | 0.96 | 0.93 | |||
IG-ResNeXt-101 [62] | AKIEC | 70.45 | 99.77 | 99.32 | 91.80 | 82.12 | 0.61 | 0.76 | 0.76 | 0.92 |
BCC | 89.83 | 99.46 | 98.93 | 90.54 | 0.82 | 0.90 | 0.90 | |||
BKL | 81.72 | 99.05 | 98.33 | 78.97 | 0.67 | 0.80 | 0.79 | |||
DF | 78.72 | 99.94 | 99.85 | 84.05 | 0.69 | 0.81 | 0.81 | |||
MEL | 77.00 | 98.13 | 96.32 | 79.38 | 0.64 | 0.78 | 0.76 | |||
NV | 91.68 | 96.68 | 95.14 | 92.48 | 0.85 | 0.92 | 0.89 | |||
SCC | 77.05 | 99.87 | 99.63 | 86.24 | 0.69 | 0.81 | 0.81 | |||
SK | 44.71 | 99.72 | 99.31 | 53.52 | 0.32 | 0.49 | 0.49 | |||
VASC | 88.00 | 100.00 | 99.95 | 100.00 | 0.88 | 0.94 | 0.49 | |||
INDB | 97.61 | 96.11 | 96.81 | 95.68 | 0.93 | 0.97 | 0.94 | |||
SWSL-ResNeXt-101 [63] | AKIEC | 69.89 | 99.77 | 99.32 | 91.46 | 82.55 | 0.61 | 0.76 | 0.76 | 0.91 |
BCC | 89.67 | 99.56 | 99.01 | 92.12 | 0.83 | 0.91 | 0.90 | |||
BKL | 81.11 | 99.02 | 98.27 | 78.22 | 0.66 | 0.80 | 0.79 | |||
DF | 95.74 | 99.85 | 99.83 | 71.43 | 0.69 | 0.82 | 0.83 | |||
MEL | 75.30 | 98.01 | 96.07 | 78.03 | 0.62 | 0.77 | 0.75 | |||
NV | 90.96 | 96.82 | 95.01 | 92.71 | 0.85 | 0.92 | 0.88 | |||
SCC | 79.51 | 99.80 | 99.59 | 80.83 | 0.67 | 0.80 | 0.80 | |||
SK | 42.35 | 99.70 | 99.28 | 50.70 | 0.30 | 0.46 | 0.46 | |||
VASC | 88.00 | 99.97 | 99.91 | 91.67 | 0.81 | 0.90 | 0.90 | |||
INDB | 97.59 | 95.76 | 96.62 | 95.31 | 0.93 | 0.96 | 0.93 | |||
Inception-ResNet-v2 [64] | AKIEC | 43.18 | 99.83 | 98.97 | 87.74 | 79.17 | 0.39 | 0.56 | 0.58 | 0.88 |
BCC | 71.83 | 99.49 | 97.98 | 89.13 | 0.66 | 0.80 | 0.79 | |||
BKL | 72.28 | 98.46 | 97.37 | 67.18 | 0.53 | 0.70 | 0.68 | |||
DF | 80.85 | 99.91 | 99.84 | 79.17 | 0.67 | 0.80 | 0.80 | |||
MEL | 62.10 | 98.35 | 95.25 | 77.92 | 0.53 | 0.69 | 0.67 | |||
NV | 91.04 | 92.83 | 92.28 | 84.96 | 0.78 | 0.88 | 0.82 | |||
SCC | 59.02 | 99.79 | 99.37 | 75.00 | 0.49 | 0.66 | 0.66 | |||
SK | 30.59 | 99.78 | 99.27 | 50.00 | 0.23 | 0.38 | 0.39 | |||
VASC | 88.00 | 99.90 | 99.85 | 78.57 | 0.71 | 0.83 | 0.83 | |||
INDB | 96.51 | 94.26 | 95.31 | 93.69 | 0.91 | 0.95 | 0.91 | |||
ReXNet_1.5 [65] | AKIEC | 64.77 | 99.51 | 98.99 | 89.20 | 67.06 | 0.49 | 0.66 | 0.65 | 0.89 |
BCC | 79.66 | 99.40 | 98.32 | 88.52 | 0.72 | 0.84 | 0.83 | |||
BKL | 71.46 | 98.76 | 97.62 | 71.46 | 0.56 | 0.71 | 0.70 | |||
DF | 68.09 | 99.97 | 99.84 | 88.89 | 0.63 | 0.77 | 0.78 | |||
MEL | 63.60 | 98.12 | 95.16 | 75.99 | 0.53 | 0.69 | 0.67 | |||
NV | 91.37 | 94.23 | 93.35 | 87.57 | 0.81 | 0.89 | 0.85 | |||
SCC | 68.85 | 99.81 | 99.49 | 79.25 | 0.58 | 0.74 | 0.74 | |||
SK | 34.12 | 99.89 | 99.41 | 60.05 | 0.30 | 0.46 | 0.48 | |||
VASC | 88.00 | 99.95 | 99.90 | 88.00 | 0.79 | 0.88 | 0.88 | |||
INDB | 97.42 | 95.35 | 96.32 | 94.88 | 0.93 | 0.96 | 0.93 | |||
SMVE (Ours) | AKIEC | 70.45 | 99.83 | 99.38 | 92.33 | 86.11 | 0.63 | 0.78 | 0.78 | |
BCC | 90.61 | 99.56 | 99.07 | 92.34 | 0.84 | 0.91 | 0.91 | |||
BKL | 80.70 | 99.20 | 98.43 | 81.54 | 0.68 | 0.81 | 0.80 | |||
DF | 89.36 | 99.96 | 99.91 | 89.36 | 0.81 | 0.89 | 0.89 | |||
MEL | 74.10 | 98.63 | 96.53 | 83.54 | 0.65 | 0.79 | 0.77 | |||
NV | 93.16 | 96.47 | 95.45 | 92.16 | 0.86 | 0.93 | 0.89 | |||
SCC | 81.15 | 99.89 | 99.69 | 88.39 | 0.73 | 0.85 | 0.85 | |||
SK | 42.35 | 99.79 | 99.37 | 60.00 | 0.33 | 0.50 | 0.50 | |||
VASC | 94.00 | 100.00 | 99.97 | 100.00 | 0.94 | 0.97 | 0.97 | |||
INDB | 98.10 | 95.73 | 96.84 | 95.30 | 0.94 | 0.97 | 0.94 | |||
WMVE (Ours) | AKIEC | 73.30 | 99.83 | 99.43 | 92.84 | 87.16 | 0.66 | 0.80 | 0.80 | |
BCC | 91.24 | 99.57 | 99.12 | 92.54 | 0.85 | 0.92 | 0.91 | |||
BKL | 82.75 | 99.29 | 98.60 | 83.61 | 0.71 | 0.83 | 0.82 | |||
DF | 93.62 | 99.96 | 99.93 | 89.80 | 0.85 | 0.92 | 0.92 | |||
MEL | 76.90 | 98.63 | 96.77 | 84.04 | 0.67 | 0.80 | 0.79 | |||
NV | 93.18 | 96.88 | 95.74 | 93.00 | 0.87 | 0.93 | 0.90 | |||
SCC | 82.79 | 99.88 | 99.70 | 87.83 | 0.74 | 0.85 | 0.85 | |||
SK | 41.18 | 99.78 | 99.35 | 57.38 | 0.32 | 0.48 | 0.48 | |||
VASC | 92.00 | 99.98 | 99.95 | 95.83 | 0.88 | 0.94 | 0.94 | |||
INDB | 98.26 | 96.03 | 97.08 | 95.63 | 0.94 | 0.97 | 0.94 |
Reference | Algorithm | No of Classes | Data | Avg Sn (%) | Avg Sp (%) | Avg Acc (%) | MAcc (%) | Avg Pr (%) | Avg Ji | Avg DSC | MCC |
---|---|---|---|---|---|---|---|---|---|---|---|
[40] | Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201 | 7 | ISIC_2018 | 88.44 | |||||||
[52] | EfficientNetB0, EfficientNetB1 and SeReNeXt-50 | 7 | ISIC_2018 | 96.30 | 86.20 | 91.30 | |||||
[75] | Kernel Extreme Learning Machine, ResNet101 and DenseNet201 | 7 | HAM_10000 | 90.20 | 90.67 | ||||||
[76] | EW-FCM and EfficientNet-B0 | 8 | HAM_10000 | 87.23 | 97.87 | 87.23 | |||||
[77] | GoogleNet and SVM | 8 | ISIC_2019 | 79.80 | 97.00 | 94.92 | 80.36 | ||||
[78] | KNN and SVM | 8 | ISIC_2019 | 66.45 | 97.85 | 97.35 | 91.61 | ||||
[86] | InceptionResNetV2 and ResNeXt101 | 7 | ISIC_2019 | 85.00 | 92.83 | 83.00 | 0.84 | ||||
[87] | EfficientNets, SENet, and ResNeXt WSL | 9 | ISIC_2019 | 74.20 | |||||||
[88] | DenseNet201 and MobileNetV2 | ISIC_2018, ISIC_2019 | 94.50 | ||||||||
[89] | VGG16, VGG19, ResNet50, ResNet101, ResNet152, Xception, and MobileNet | 7 | HAM_10000 | 83.69 | |||||||
[90] | AlexNet, ResNet18, ResNet50, ResNet101, ResNet152, SENet154, SqueezeNet1_0, VGG13BN and VGG16BN | 7 | ISIC_2018, ISIC_2019 | 48.30 | 97.70 | 92.40 | 0.49 | ||||
[91] | MobileNetV2 and GoogLeNet | 7 | HAM_10000 | 65.60 | 95.40 | 83.50 | 76.60 | 0.69 | 0.66 | ||
SMVE (Ours) | IG-ResNeXt-101, SWSL-ResNeXt-101, ECA-ResNet-101, and DPN-131 | 10 | ISIC_2016, ISIC_2017, ISIC_2018, ISIC_2019, ISIC_2020 | 81.40 | 98.91 | 98.46 | 92.33 | 86.87 | 0.74 | 0.84 | 0.83 |
WMVE (Ours) | IG-ResNeXt-101, SWSL-ResNeXt-101, ECA-ResNet-101, and DPN-131 | 10 | ISIC_2016, ISIC_2017, ISIC_2018, ISIC_2019, ISIC_2020 | 82.52 | 99.00 | 98.54 | 92.84 | 86.68 | 0.75 | 0.84 | 0.84 |
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Okuboyejo, D.A.; Olugbara, O.O. Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning. Algorithms 2022, 15, 443. https://doi.org/10.3390/a15120443
Okuboyejo DA, Olugbara OO. Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning. Algorithms. 2022; 15(12):443. https://doi.org/10.3390/a15120443
Chicago/Turabian StyleOkuboyejo, Damilola A., and Oludayo O. Olugbara. 2022. "Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning" Algorithms 15, no. 12: 443. https://doi.org/10.3390/a15120443