WaveletBased Classification of Enhanced Melanoma Skin Lesions through Deep Neural Architectures
Abstract
:1. Introduction
2. Related Works
2.1. Need for the Study
2.2. Contribution of the Research Article
 (i)
 A novel preprocessing method was used as a basis for median filtering. The traditional median filter was hybridized with the Range method (Algorithm 1), Fuzzy Relational method (Algorithm 2), and Similarity coefficient method (Algorithm 3);
 (ii)
 Segmentation was imparted using Normalized Otsu’s segmentation [18];
 (iii)
 Feature extraction was performed with Wavelet coefficients (DB4, Symlets, RBIO);
 (iv)
 Classification was performed using ANN, SVM, and ANFIS. The proposed algorithms were implemented with melanoma skin lesion images and enhanced for further processing. The quality factor of the enhanced image was then measured with statistical measures such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).
3. Proposed Methodology
3.1. Image Enhancement through an Enhanced Median Filter
3.1.1. Algorithm for Range Method
Algorithm 1: Range Method. 
Input: Gray scale image of melanoma/benign skin lesion Output: Enhanced image

3.1.2. Algorithm for Fuzzy Relational Method
Algorithm 2: Fuzzy Relational Method. 
Input: Gray scale image of melanoma/benign skin lesion Output: Enhanced image

3.1.3. Algorithm for Similarity Coefficient Method
Algorithm 3: Similarity Coefficient Method. 
Input: Grayscale image of melanoma/benign skin lesion Output: Enhanced image

3.2. Segmentation
3.2.1. Entropy Features
3.2.2. Approximate Entropy (ApEn)
3.2.3. Sample Entropy (SamEn)
3.2.4. Shannon Entropy (ShEn)
3.2.5. Log Energy Entropy (LogEn)
3.2.6. Threshold Entropy (ThEn)
3.2.7. Sure Entropy (SrEn)
3.2.8. Norm Entropy (NmEn)
3.3. Statistical Features
 $\mathrm{Mean}\text{}\left(\mathrm{i}\right)=\frac{{{\displaystyle \sum}}_{\mathrm{m}=1}^{\mathrm{M}}{{\displaystyle \sum}}_{\mathrm{n}=1}^{\mathrm{N}}{\mathrm{x}}_{\mathrm{mn}}}{\mathrm{M}\text{}\times \text{}\mathrm{N}}\text{}$ where i = matrix of low/highfrequency components, ${\mathrm{x}}_{\mathrm{mn}}$ = matrix element, M × N is the size of the coefficient matrix.
 $\mathrm{Median}=\mathrm{Center}\text{}\mathrm{value}\text{}\mathrm{of}\text{}\mathrm{a}\text{}\mathrm{vector}$ if the vector has an odd number of values. $\mathrm{Median}=\frac{\mathrm{m}+\mathrm{n}}{2}$, where m, n = two mid values if the vector has an even number of values. The median of the matrix gives the central tendency of the matrix.
 Standard deviation $\sqrt{\frac{1}{\mathrm{mn}1}{{\displaystyle \sum}}_{\left(\mathrm{r},\mathrm{c}\right)\in \mathrm{W}}{\left(\mathrm{g}\left(\mathrm{r},\mathrm{c}\right)\frac{1}{\mathrm{mn}1}{{\displaystyle \sum}}_{\left(\mathrm{r},\mathrm{c}\right)\in \mathrm{W}}\mathrm{g}\left(\mathrm{r},\mathrm{c}\right)\right)}^{2}}$, where m $\times $ n = Window size, $\mathrm{g}\left(\mathrm{r},\mathrm{c}\right)$ represent the Input of r rows and c columns.
 The median absolute deviation is the measure of average absolute deviations from a central point with respect to the median. It is defined as the $\mathrm{median}.\text{}\mathrm{abs}.\mathrm{dev}=\frac{1}{\mathrm{mn}}{{\displaystyle \sum}}_{\mathrm{i}}^{\mathrm{m}}{{\displaystyle \sum}}_{\mathrm{j}}^{\mathrm{n}}\left{\mathrm{x}}_{\mathrm{ij}}\mathrm{m}\left(\mathrm{X}\right)\right$ where m(X) = median of the values in a matrix or dataset, ${\mathrm{x}}_{\mathrm{ij}}$ = element of a matrix, and mn = total number of elements.
 Mean absolute deviation also provides the average absolute deviations from a central point with respect to the mean value of the matrix. It is defined as $\mathrm{mean}.\mathrm{abs}.\mathrm{dev}=\frac{1}{\mathrm{mn}}{{\displaystyle \sum}}_{\mathrm{i}}^{\mathrm{m}}{{\displaystyle \sum}}_{\mathrm{j}}^{\mathrm{n}}\left{\mathrm{x}}_{\mathrm{ij}}\mathrm{m}\left(\mathrm{X}\right)\right$ where m(X) = mean, ${\mathrm{x}}_{\mathrm{ij}}$ = element of a matrix, mn = total number of elements.
 Mathematically, the Norm is the total length of all the vectors in a vector space or matrices. The higher the norm value, the bigger the matrix is. Here, L1 norm and L2 norm were derived for the wavelet coefficients.
 L1 norm is also called Sum Absolute Difference, and it is the difference between two vectors which can be defined as ${\Vert {x}_{1}{x}_{2}\Vert}_{1}={{\displaystyle \sum}}_{i}{x}_{1i}{x}_{2i}$ where x = elements of the vector and i = index value.
 L2 norm is generally called the Euclidean norm, and it gives the vector difference. It is a sum of squared difference denoted by ${\Vert {x}_{1}{x}_{2}\Vert}_{2}=\sqrt{{{\displaystyle \sum}}_{i}{({x}_{1i}{x}_{2i})}^{2}}$ x = elements of the vector, and i = index value. The range is the takeaway between the maximum and minimum value of the vector space, and it is defined by $range=\mathrm{max}\left(X\right)\mathrm{min}\left(X\right),\text{}X=\left\{{x}_{1},{x}_{2},\text{}\dots ,{x}_{i}\right\}$.
Feature Selection
3.4. Classification
ANFIS
3.5. SVM
4. Experimentation Results
4.1. Normalized Otsu’s Segmentation
4.2. Discussion
 The classification accuracy obtained from DLNN through the Symlet function was higher than all other machine learning algorithms for the used dataset.
 Clearly, selecting entropybased features yielded higher classification accuracy than selecting the mean and variance of the wavelet coefficients.
 We obtained a subtle difference (0.07%) between the spatial and frequency domain classification accuracy.
4.3. Limitations
5. Conclusions and Future Work
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Entropy Features  Statistical Features 



Images  Traditional Median Filter  Range Method  Fuzzy Relational Method  Similarity Coefficient Method  

PSNR  MSE  PSNR  MSE  PSNR  MSE  PSNR  MSE  
1.jpg  18.21  7.78  23.63  2.04  21.13  6.04  20.15  6.44 
2.jpg  18.67  21.91  25.68  17.90  23.68  20.79  22.81  21.2 
3.jpg  17.74  43.67  22.52  41.73  20.02  42.71  18.97  42.23 
4.jpg  18.43  13.75  25.43  10.09  23.38  12.99  22.53  12.59 
5.jpg  18.10  17.46  23.09  13.99  21.29  16.55  20.42  18.39 
6.jpg  18.33  8.93  24.32  4.37  22.32  8.26  21.27  7.67 
7.jpg  17.74  22.60  22.68  19.39  20.88  16.72  20.03  19.89 
8.jpg  18.81  31.86  26.82  28.98  24.78  27.08  23.91  31.48 
9.jpg  17.77  22.33  22.77  19.26  20.97  16.26  19.92  23.66 
10.jpg  18.65  20.01  25.89  15.94  23.69  12.34  22.84  19.24 
S.no  Classification Technique  Accuracy (%)  Sensitivity (%)  Specificity (%)  Kappa (%)  Precision (%)  F1 Score (%)  Training Time (in Minutes)  Testing Time (in Seconds)  

1.  SVM [41]  80.00  86.29  55.36  73.05  86.21  71.43  46.42  379  
2.  DCNN [42]  81.41  81.88  89.12  81.80  81.30  81.05  48.64  372  
3.  Neural Network [43]  91.25  91.32  90.03  89.21  91.97  91.47  49.03  362  
4.  SVM QuadTree Tree [44]  86.04  93.44  68.00  78.07  87.69  90.47  48.42  396  
Proposed Methodologies  DB  ANN  85.75  88.70  82.30  71.20  85.69  87.17  48.82  360 
ANFIS  84.51  87.85  80.53  68.60  84.08  85.92  48.96  374  
SVM  89.32  90.96  87.47  78.50  90.14  90.55  44.01  342  
DLNN  86.50  88.85  83.63  72.70  86.94  87.88  38.99  264  
Real AdaBoost  84.41  87.85  80.53  68.60  84.08  85.92  46.49  392  
Modest AdaBoost  84.46  87.85  80.53  68.60  84.08  85.92  46.21  388  
Gentle AdaBoost  87.62  89.93  84.98  75.10  87.99  88.95  45.95  372  
Hybrid AdaBoost  90.24  91.99  88.04  80.20  90.50  91.24  46.36  391  
Symlet  ANN  90.21  91.99  88.04  80.20  90.50  91.24  48.62  362  
ANFIS  89.41  90.96  87.47  78.50  90.14  90.55  48.92  381  
SVM  89.92  91.32  87.03  79.30  90.50  90.99  44.21  333  
DLNN  93.62  94.59  92.45  87.10  94.09  94.34  38.90  252  
Real AdaBoost  86.73  89.17  83.67  73.00  86.94  88.04  46.81  382  
Modest AdaBoost  87.04  88.95  84.77  73.80  87.99  88.47  46.49  372  
Gentle AdaBoost  90.13  91.82  88.01  80.00  90.50  91.16  46.32  370  
Hybrid AdaBoost  91.88  92.65  90.55  83.20  92.65  92.65  46.63  394  
RBIO  ANN  86.39  89.11  83.11  72.50  86.40  87.74  49.02  359  
ANFIS  87.65  89.93  84.98  75.10  87.99  88.95  49.89  390  
SVM  89.52  90.97  87.67  78.70  90.32  90.65  45.06  352  
DLNN  89.44  90.96  87.47  78.50  90.14  90.55  39.04  277  
Real AdaBoost  84.95  88.51  80.69  69.50  84.08  86.24  46.96  394  
Modest AdaBoost  86.31  89.11  83.11  72.50  86.40  87.74  46.21  382  
Gentle AdaBoost  89.69  90.97  87.67  78.70  90.32  90.65  47.33  399  
Hybrid AdaBoost  90.17  91.82  88.01  80.00  90.50  91.16  47.04  401 
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Jayaraman, P.; Veeramani, N.; Krishankumar, R.; Ravichandran, K.S.; Cavallaro, F.; Rani, P.; Mardani, A. WaveletBased Classification of Enhanced Melanoma Skin Lesions through Deep Neural Architectures. Information 2022, 13, 583. https://doi.org/10.3390/info13120583
Jayaraman P, Veeramani N, Krishankumar R, Ravichandran KS, Cavallaro F, Rani P, Mardani A. WaveletBased Classification of Enhanced Melanoma Skin Lesions through Deep Neural Architectures. Information. 2022; 13(12):583. https://doi.org/10.3390/info13120583
Chicago/Turabian StyleJayaraman, Premaladha, Nirmala Veeramani, Raghunathan Krishankumar, Kattur Soundarapandian Ravichandran, Fausto Cavallaro, Pratibha Rani, and Abbas Mardani. 2022. "WaveletBased Classification of Enhanced Melanoma Skin Lesions through Deep Neural Architectures" Information 13, no. 12: 583. https://doi.org/10.3390/info13120583