Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN
Abstract
:1. Introduction
2. Methods
2.1. Dataset
2.2. Data Preprocessing
- ❖
- The pixels of the skin lesion domain are taken to a fuzzy domain. Let M be an image of p × q, and M (p,q) represent the intensity of the skin lesion image pixels that must be mapped to the fuzzy characteristic plane. It can be expressed as follows , p = 1, …, m and q = 1, …, n; where represent the pixels and µM (p, q) is the intensity level degree of the image ranging from zero to one.
- ❖
- Assign the fuzzy plane pixels to the logarithmic function to map to the fuzzy domain f (M (p, q)) = ((1 + ); where Mmax and Mmin are the maximum and minimum intensity of the skin lesion image pixels.
- ❖
- To enhance the portions of the skin lesion images, transform the image using the trigonometric series with fuzzy principles as mentioned f (T (p, q)) = T (p, q) + f (M (p, q))2 where 0 ≤ f (M (p, q)) ≤ 0.5; where T (p, q) = and a = π (f (M (p, q) − 0.5) + 1.
- ❖
- The defuzzification can be expressed as D = Mmin + ((Mmax − Mmin) × 2 T(p, q) − 1)
- ❖
- Later, enhance the image quality by skin lesion image channel-wise.
2.3. Image Segmentation
2.4. Feature Extraction
Algorithm 1: GC-SCNN. |
Input: Segmented Images Output: Skin cancer Classification results for k = 1 to length (segmented images) do for j = 1 to 3 do sub-model j. predicts (segmented image) end for final = concatenation (P1, P2, P3) end for assess the SoftMax classifier on the feature vector final stacked CNN = Train (final, label) classification of skin cancer images prediction = classification (stacked CNN, testset) return prediction |
2.5. Lesion Classification
Algorithm 2: Enhanced SVM algorithm. |
Initialize the values in the training set Repeat for every i = 1 to N calculate the loss function for all values compare the extracted patches in the images end for Repeat for every score vector i- 1 to N Compute SVM with imputed labels argmax((w × xi) + b), i end for Evaluate for different weights and compute output. |
2.6. Experimental Framework
2.7. Performance Metrics
- ❖
- Accuracy measures the portion of the true results among the total number of the cases and is written as accuracy = ; Where FP-False positive, FN-False Negative, TP-True Positive, TN-True Negative
- ❖
- Sensitivity is the portion of the positive outcomes among the actual positive and it is defined as sensitivity =
- ❖
- Specificity is defined as the portion of the true negative outcomes among the negative outcomes and it is written as specificity =
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Batch Size | Optimizer | Dense | Learning Rate | Weight Decay Values | Epoch | Loss | Processing Time (ms) |
---|---|---|---|---|---|---|---|
32 | RMSProp | 4 | 0.0001 | 0.0001 | 50 | 6.50 | 4 |
RMSProp | 4 | 0.0001 | 0.001 | 100 | 6.56 | 5 | |
RMSProp | 4 | 0.001 | 0.0001 | 50 | 6.25 | 4 | |
RMSProp | 4 | 0.001 | 0.001 | 100 | 6.57 | 5 | |
64 | RMSProp | 4 | 0.0001 | 0.0001 | 50 | 7.27 | 5 |
RMSProp | 4 | 0.0001 | 0.001 | 100 | 7.37 | 6 | |
RMSProp | 4 | 0.001 | 0.0001 | 50 | 7.52 | 5 | |
RMSProp | 4 | 0.001 | 0.001 | 100 | 7.79 | 7 | |
32 | RMSProp | 5 | 0.0001 | 0.0001 | 50 | 8.46 | 6 |
RMSProp | 5 | 0.0001 | 0.001 | 100 | 8.63 | 7 | |
RMSProp | 5 | 0.001 | 0.0001 | 50 | 8.21 | 6 | |
RMSProp | 5 | 0.001 | 0.001 | 100 | 8.35 | 7 | |
64 | RMSProp | 5 | 0.0001 | 0.0001 | 50 | 8.32 | 8 |
RMSProp | 5 | 0.0001 | 0.001 | 100 | 8.34 | 7 | |
RMSProp | 5 | 0.001 | 0.0001 | 50 | 8.25 | 7 | |
RMSProp | 5 | 0.001 | 0.001 | 100 | 8.31 | 7 | |
32 | ADAM | 4 | 0.0001 | 0.0001 | 50 | 6.26 | 3 |
ADAM | 4 | 0.0001 | 0.001 | 100 | 6.28 | 4 | |
ADAM | 4 | 0.001 | 0.0001 | 50 | 6.27 | 4 | |
ADAM | 4 | 0.001 | 0.001 | 100 | 6.55 | 5 | |
64 | ADAM | 4 | 0.0001 | 0.0001 | 50 | 7.04 | 4 |
ADAM | 4 | 0.0001 | 0.001 | 100 | 7.06 | 5 | |
ADAM | 4 | 0.001 | 0.0001 | 50 | 7.26 | 4 | |
ADAM | 4 | 0.001 | 0.001 | 100 | 7.27 | 6 | |
32 | ADAM | 5 | 0.0001 | 0.0001 | 50 | 7.67 | 4 |
ADAM | 5 | 0.0001 | 0.001 | 100 | 7.63 | 5 | |
ADAM | 5 | 0.001 | 0.0001 | 50 | 7.21 | 4 | |
ADAM | 5 | 0.001 | 0.001 | 100 | 7.35 | 6 | |
64 | ADAM | 5 | 0.0001 | 0.0001 | 50 | 8.02 | 4 |
ADAM | 5 | 0.0001 | 0.001 | 100 | 8.14 | 5 | |
ADAM | 5 | 0.001 | 0.0001 | 50 | 8.05 | 5 | |
ADAM | 5 | 0.001 | 0.001 | 100 | 8.10 | 6 | |
32 | AdaGrad | 4 | 0.0001 | 0.0001 | 50 | 6.47 | 4 |
AdaGrad | 4 | 0.0001 | 0.001 | 100 | 6.74 | 4 | |
AdaGrad | 4 | 0.001 | 0.0001 | 50 | 6.25 | 5 | |
AdaGrad | 4 | 0.001 | 0.001 | 100 | 6.55 | 5 | |
64 | AdaGrad | 4 | 0.0001 | 0.0001 | 50 | 7.14 | 5 |
AdaGrad | 4 | 0.0001 | 0.001 | 100 | 7.06 | 6 | |
AdaGrad | 4 | 0.001 | 0.0001 | 50 | 7.16 | 6 | |
AdaGrad | 4 | 0.001 | 0.001 | 100 | 7.29 | 7 | |
32 | AdaGrad | 5 | 0.0001 | 0.0001 | 50 | 7.77 | 5 |
AdaGrad | 5 | 0.0001 | 0.001 | 100 | 7.61 | 6 | |
AdaGrad | 5 | 0.001 | 0.0001 | 50 | 7.23 | 6 | |
AdaGrad | 5 | 0.001 | 0.001 | 100 | 7.32 | 7 | |
64 | AdaGrad | 5 | 0.0001 | 0.0001 | 50 | 8.06 | 5 |
AdaGrad | 5 | 0.0001 | 0.001 | 100 | 8.18 | 5 | |
AdaGrad | 5 | 0.001 | 0.0001 | 50 | 8.09 | 6 | |
AdaGrad | 5 | 0.001 | 0.001 | 100 | 8.11 | 7 | |
32 | Adadelta | 4 | 0.0001 | 0.0001 | 50 | 6.69 | 4 |
Adadelta | 4 | 0.0001 | 0.001 | 100 | 6.56 | 5 | |
Adadelta | 4 | 0.001 | 0.0001 | 50 | 6.28 | 4 | |
Adadelta | 4 | 0.001 | 0.001 | 100 | 6.47 | 4 | |
64 | Adadelta | 4 | 0.0001 | 0.0001 | 50 | 6.85 | 4 |
Adadelta | 4 | 0.0001 | 0.001 | 100 | 7.44 | 4 | |
Adadelta | 4 | 0.001 | 0.0001 | 50 | 7.16 | 5 | |
Adadelta | 4 | 0.001 | 0.001 | 100 | 7.26 | 5 | |
32 | Adadelta | 5 | 0.0001 | 0.0001 | 50 | 7.67 | 6 |
Adadelta | 5 | 0.0001 | 0.001 | 100 | 7.77 | 6 | |
Adadelta | 5 | 0.001 | 0.0001 | 50 | 7.73 | 5 | |
Adadelta | 5 | 0.001 | 0.001 | 100 | 7.31 | 7 | |
64 | Adadelta | 5 | 0.0001 | 0.0001 | 50 | 7.55 | 6 |
Adadelta | 5 | 0.0001 | 0.001 | 100 | 8.08 | 7 | |
Adadelta | 5 | 0.001 | 0.0001 | 50 | 8.19 | 5 | |
Adadelta | 5 | 0.001 | 0.001 | 100 | 8.12 | 6 |
Classifier | Accuracy (%) | Sensitivity (%) | Specificity (%) |
---|---|---|---|
DCN transfer learning [29] | 94.92 | 80.36 | 79.8 |
Mobile Net [30] | 83.1 | 89 | 83 |
Kernel extreme learning machine [31] | 90.67 | 90.20 | 89.43 |
DilatInceptV3 [32] | 90.10 | 87 | 87 |
Proposed | 99.75 | 100 | 100 |
Project | Accuracy (%) | Sensitivity (%) | Specificity (%) |
---|---|---|---|
Gessert et al. [33] | 98.70 | 80.9 | 98.4 |
Ailin et al. [34] | 98.20 | 89.5 | 98.1 |
Khan et al. [35] | 89.80 | 89.7 | 94.5 |
Mohamed et al. [36] | 92.70 | 72.42 | 97.14 |
Huang et al. [37] | 85.80 | 69.04 | 95.92 |
Liu et al. [38] | 92.54 | 71.47 | 92.72 |
Gu et al. [39] | 91.4 | 83.74 | 93.24 |
Zhou et al. [40] | 92.55 | 84.67 | 93.63 |
Gan et al. [41] | 93.81 | 90.14 | 98.36 |
Proposed | 99.78 | 100 | 100 |
Project | Accuracy (%) | Sensitivity (%) | Specificity (%) |
---|---|---|---|
Gessert et al. [33] | 92.3 | 80.9 | 98.4 |
Ailin et al. [34] | 91.5 | 89.5 | 98.1 |
Ahmed et al. [42] | 94 | 89.7 | 94.5 |
Pacheco et al. [43] | 92 | 72.42 | 97.14 |
Molina et al. [44] | 97 | 69.04 | 95.92 |
Kaseem et al. [45] | 94 | 71.47 | 92.72 |
Iqbla et al. [46] | 90 | 83.74 | 93.24 |
Pulgarin et al. [47] | 92 | 89.53 | 93.57 |
Proposed | 99.51 | 100 | 100 |
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Bhimavarapu, U.; Battineni, G. Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN. Healthcare 2022, 10, 962. https://doi.org/10.3390/healthcare10050962
Bhimavarapu U, Battineni G. Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN. Healthcare. 2022; 10(5):962. https://doi.org/10.3390/healthcare10050962
Chicago/Turabian StyleBhimavarapu, Usharani, and Gopi Battineni. 2022. "Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN" Healthcare 10, no. 5: 962. https://doi.org/10.3390/healthcare10050962