# An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

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## Abstract

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## 1. Introduction

## 2. Literature Review

## 3. Materials

^{2}[29] dataset. “11k hands” publicly available dataset repository is used for healthy images. Some of the images related to eczema and healthy category are collected from researchers [30] working in the field of skin lesions classification. IEEE International Symposium on Biomedical Imaging (ISBI) skin lesion challenge [31] is an international skin lesion classification challenge organized every year since 2016. Some of the images related to benign and malignant class were used from ISBI skin lesions repository. Figure 1 graphically presents the images belonging to different categories. After collecting all the data from different sources, a uniformed dataset has been created for this work.

## 4. Method

#### 4.1. Pre-Processing and Segmentation

#### 4.2. Feature Extraction

#### 4.2.1. Colour Features

#### 4.2.2. Texture Features

#### 4.3. Classification

## 5. Results and Discussion

^{th}subset is used for the testing and k-1 subsets are used for training, and finally, the average performance across all k trial is calculated. Using 35 colour and texture features, SVM with quadratic kernel performed best among all classifiers. As mentioned above, after performing classification, a multi-class confusion matrix was obtained for each classifier. The confusion matrix for fine tree, quadratic SVM, weighted KNN and bagged trees are provided in the Supplementary Material. The training time required by the SVM with the quadratic kernel was 3.0624 sec whereas the prediction speed was approximately 8400 obs/sec (observations per second). Among decision tree classifiers, fine tree gives the highest accuracy. The average per-class accuracy achieved by fine tree was 88.40%. The sensitivity and specificity obtained by fine tree was 70.24% and 93.04% respectively. The computational time for training the classification model was 3.4608 sec. The maximum number of splits used while using fine tree was 10. As mentioned earlier, among the SVM, Quadratic kernel performed better than others. The accuracy, sensitivity, and specificity achieved by quadratic SVM kernel was 94.74%, 84.23% and 96.85%. The training time for quadratic SVM was 3.0624 sec. For the KNN, weighted KNN performed better with an average per-class accuracy, sensitivity, and specificity of 92.80%, 78.38%, and 95.68% respectively. For weighted KNN, experiments were performed using Euclidean distance and 10 neighbors. The performance of the bagged trees was almost similar to quadratic SVM, and 94.16% accuracy, 82.48% sensitivity, and 96.49% specificity was attained. The results of the fine tree, quadratic SVM, weighted KNN and bagged trees are given in Table 7.

## 6. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Steps involved in the proposed classification framework (1. Preprocessing, 2. Segmentation, 3. Feature Extraction, 4. Classification).

**Table 1.**Number of images used in healthy, acne, eczema, psoriasis, benign and malignant categories.

Category | No. of Images |
---|---|

Healthy | 300 |

Acne | 300 |

Eczema | 300 |

Psoriasis | 300 |

Benign | 300 |

Malignant | 300 |

Total | 1800 |

**Table 2.**Different colour features extracted from red, green and blue colour space along with their description and formulae. The colour features include minimum, maximum, mean, mode, standard deviation, skewness, energy, entropy and kurtosis).

Feature Name | Description | Formula |
---|---|---|

Min | Minimum pixel value of R, G and B colour | Min(colour space) |

Max | Maximum pixel value of R, G and B colour | Max(colour space) |

Mean | Measures image overall intensity | $M\left(\overline{g}\right)={\sum}_{r}{\sum}_{c}\frac{I\left(r,c\right)}{M}$ |

Mode | Gives information about the most occurring value | Mode(colour space) |

Standard Deviation | Presents the spread of the data | ${\sigma}_{g}=\sqrt{{\displaystyle \sum _{g=0}^{W-1}}{\left(g-\overline{g}\right)}^{2}P\left(g\right)}$ |

Skewness | Measure asymmetry of the probability distribution | $=\frac{1}{{\sigma}^{3}}{\displaystyle \sum _{g=0}^{W-1}}{\left(g-\overline{g}\right)}^{3}P\left(g\right)$ |

Energy | Gives information about the spread of the pixel values | $={\displaystyle \sum _{g=0}^{W-1}}{\left[P\left(g\right)\right]}^{2}$ |

Entropy | Measure the required amount of information to code the image data | $=-{\displaystyle \sum _{g=0}^{w-1}}\left[P\left(g\right){\mathrm{log}}_{2}P\left(g\right)\right]$ |

Kurtosis | Measure of the peakness of the probability distribution of an image | $=\frac{1}{{\sigma}^{4}}{\displaystyle \sum _{g=0}^{W-1}}{\left(g-\overline{g}\right)}^{4}P\left(g\right)$ |

Legends*: $w$ is the number of intensity levels, $g$ is the intensity level, $r$ is the number of rows, $c$ is the number of columns in the image, $\overline{g}$ is the mean, ${\sigma}_{g}$ is the standard deviation |

**Table 3.**GLCM features with their description and formulae. GLCM features include contrast, correlation, energy and homogeneity.

Name | Description | Formula |
---|---|---|

ContrastGLCM | Measure the local fluctuations of grey levels of neighbor pixels | $\sum _{i,j=0}^{W-1}}{P}_{ij}{\left(i-j\right)}^{2$ |

CorrelationGLCM | Measure the joint probability occurrence of specified pair pixels | $\sum _{i,j=0}^{W-1}}{P}_{ij}\frac{\left(i-\mu \right)\left(j-\mu \right)}{{\sigma}^{2}$ |

EnergyGLCM | Measure the sum of squared elements in the GLCM | $-{\displaystyle \sum _{g=0}^{w-1}}\left[P\left(g\right){\mathrm{log}}_{2}P\left(g\right)\right]$ |

HomogeneityGLCM | Measures the local uniformity | $\sum _{i,j=0}^{W-1}}\frac{{P}_{ij}}{1+{\left(i-j\right)}^{2}$ |

**Table 4.**Features extracted from the Neighborhood grey-tone difference matrix along with their description and formula.

Name | Description | Formula |
---|---|---|

Busyness | Measure changes in grey levels between neighboring voxels | $=\frac{{\sum}_{i=1}^{{N}_{g}}p\left(i\right)s\left(i\right)}{{\sum}_{i=1}^{{N}_{g}}{\sum}_{j=1}^{{N}_{g}}\left|ip\left(i\right)-jp\left(j\right)\right|},\text{}p\left(i\right)\ne 0,p\left(j\right)\ne 0$ |

Complexity | Measure the non-uniformity and rapid changes in grey-levels | $\begin{array}{c}=\frac{1}{{N}_{v}}\text{}{\displaystyle \sum _{i=1}^{{N}_{g}}}{\displaystyle \sum _{j=1}^{{N}_{g}}}\left|i-j\right|\frac{p\left(i\right)s\left(i\right)+p\left(j\right)s\left(j\right)}{p\left(i\right)+p\left(j\right)},\\ p\left(i\right)\ne 0,p\left(j\right)\ne 0\end{array}$ |

Contrast | Measures the changes between voxels and their neighborhood | $\begin{array}{c}=\left(\frac{1}{{N}_{p}\left(1-{N}_{p}\right)}\text{}{\displaystyle \sum _{i=1}^{{N}_{g}}}{\displaystyle \sum _{j=1}^{{N}_{g}}}p\left(i\right)p\left(j\right){\left(i-j\right)}^{2}\right)\\ \left(\frac{1}{{N}_{v}}{\displaystyle \sum _{i=1}^{{N}_{g}}}s\left(i\right)\right)\end{array}$ |

Strength | Measure the primitives in an image | $\begin{array}{c}=\frac{{\sum}_{i=1}^{{N}_{g}}{\sum}_{j=1}^{{N}_{g}}\left[p\left(i\right)+p\left(j\right)\right]{\left(i-j\right)}^{2}}{\epsilon +{\sum}_{i=1}^{{N}_{g}}s\left(i\right)},\text{}p\left(i\right)\ne 0,\\ p\left(j\right)\ne 0\end{array}$ |

Classifier | Kernel |
---|---|

Tree | Fine Tree |

Medium Tree | |

Coarse Tree | |

Support Vector Machine | Linear |

Quadratic | |

Cubic | |

Fine Gaussian | |

Coarse Gaussian | |

k-Nearest Neighbors | Fine |

Medium | |

Coarse | |

Cosine | |

Cubic | |

Weighted | |

Ensemble | Boosted Trees |

Bagged Trees | |

Subspace Discriminant | |

Subspace KNN | |

RUSBoosted Trees |

**Table 6.**Performance measures along with their formulae (TP = True Positive, TN = True Negative, FP = False Positive, FN = False Negative).

Measure | Formula | Description |
---|---|---|

$Accuracy$ | $\sum _{i}^{l}}\frac{T{P}_{i}+T{N}_{i}}{T{P}_{i}+T{N}_{i}+F{P}_{i}+F{N}_{i}$ | Measure the number of correct classifications over the total number of examples evaluated |

$Sensitivity$ | $\frac{{\sum}_{i}^{l}\frac{T{P}_{i}}{T{P}_{i}+F{N}_{i}}}{l}$ | Measure the number of actual positive cases that are correctly identified |

$Specificity$ | $\frac{{\sum}_{i}^{l}\frac{T{N}_{i}}{T{N}_{i}+F{P}_{i}}}{l}$ | Measure the number of actual negative cases that are correctly identified |

Legends: $\mathit{i}$ = Individual class i.e. Healthy, acne, eczema, psoriasis, benign and malignant $\mathit{l}$ = Total Number of classes = 6 |

**Table 7.**Performance of the different classifiers using the 10-fold cross-validation. Values depict the mean score (Standard deviation). Values in bold show the best accuracy, sensitivity and specificity score. All the score is in %.

Classifier | Accuracy (SD) | Sensitivity (SD) | Specificity (SD) |
---|---|---|---|

Fine Tree | 88.40 (0.27) | 70.24 (0.83) | 93.04 (0.17) |

Quadratic SVM | 94.74 (0.11) | 84.23 (0.32) | 96.85 (0.06) |

Weighted KNN | 92.80 (0.11) | 78.38 (0.33) | 95.68 (0.06) |

Bagged Trees | 94.16 (0.13) | 82.48 (0.39) | 96.49 (0.07) |

**Table 8.**Comparison of proposed classification framework with existing research work. All the results are in %.

Reference | Accuracy | Sensitivity | Specificity |
---|---|---|---|

[1] | 83 | NA | NA |

Proposed Work | 94.74 | 84.23 | 96.85 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Hameed, N.; Hameed, F.; Shabut, A.; Khan, S.; Cirstea, S.; Hossain, A. An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. *Computers* **2019**, *8*, 62.
https://doi.org/10.3390/computers8030062

**AMA Style**

Hameed N, Hameed F, Shabut A, Khan S, Cirstea S, Hossain A. An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. *Computers*. 2019; 8(3):62.
https://doi.org/10.3390/computers8030062

**Chicago/Turabian Style**

Hameed, Nazia, Fozia Hameed, Antesar Shabut, Sehresh Khan, Silvia Cirstea, and Alamgir Hossain. 2019. "An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions" *Computers* 8, no. 3: 62.
https://doi.org/10.3390/computers8030062