# An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics

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## Abstract

**:**

## 1. Background

## 2. Problem Statement and Contributions

## 3. Literature Review

## 4. Materials and Methodology

#### 4.1. Datasets

**ISIC 2016**: This dataset [26] comprises two sets, with 900 samples and 379 samples in the training and testing sets, respectively. This database provides ground truths for all testing and training images. As the present-day classification methods demand various classes, on the contrary, this database only comprises two classes, which fits the requirements of our proposed model.**ISIC 2017**: The set [27] comprises 2600 images, with wo sets, one for training with 2000 lesion samples and the other for testing purposes with 600 image samples, the same as the former dataset. Additionally, the dataset contains separate gold-standard images for both sets. Originally, the challenge for this dataset comprised feature identification for four classes, cancer classification for three classes and tasks such as segmentation.**ISIC 2018**: Researchers produced a database in 2018 [28] comprising two sets with around 1000 testing and 2594 training images in the first two tasks. In the third task, they introduced Ham10,000 with a huge training set of 10,000 with 1512 test images. We have used the train set produced in the first two tasks and divided the set into two parts, the reason being the lack of gold-standard samples for the test set. The division of the database is presented in Table 2.

#### 4.2. Proposed Framework

#### 4.2.1. Bat Algorithm

#### 4.2.2. Artificial Bee Colony

## 5. Results

#### 5.1. Parameter Setting

#### 5.2. Boundary Estimation Models

#### 5.2.1. First Boundary Estimation Model—BAT

#### 5.2.2. Second Boundary Estimation Model—CA-Net

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Paper | Modalities for Preprocessing |
---|---|

[33] | Colour-based histogram adjustment with thresholding based on Otsu algorithm |

[34] | Morphological operator-based hair artefact removal and image quality correction using intensity |

[35] | Geometrical-profile-based h-dome transformation followed by image histogram correction |

[36] | Colour standardisation, normalisation, bottom-hat filtration with discrete Laplacian interpolation |

[37] | Top-hat filtration followed by inpainting technique |

[38] | Three techniques: Otsu thresholding, K-means clustering and MultiResUNet |

[39] | Contrast enhancement with metaheuristic DE-BA |

[40] | Contrast enhancement with metaheuristic algorithm DE-ABC |

Database | Training Set | Testing Set | Total Images |
---|---|---|---|

ISIC-2016 | 900 | 379 | 1279 |

ISIC-2017 | 2000 | 600 | 2600 |

ISIC-2018 | 2076 | 518 | 2594 |

Sobel Kernel | Gradients |
---|---|

$\frac{\partial {{\rm Y}}_{x,y}^{e}}{\partial h}={S}_{h}\otimes {{\rm Y}}_{x,y}^{e}$ | |

$\frac{\partial {{\rm Y}}_{x,y}^{e}}{\partial v}={S}_{v}\otimes {{\rm Y}}_{x,y}^{e}$ | |

$\nabla {{\rm Y}}_{x,y}^{e}=mag\left(\right)open="["\; close="]">\frac{\partial {{\rm Y}}_{x,y}^{e}}{\partial h},\frac{\partial {{\rm Y}}_{x,y}^{e}}{\partial v}$ |

Algorithm | Dataset | IoU (%) | F1 (%) | |
---|---|---|---|---|

Without | ISIC-2016 | 85.2 | 92.0 | |

Preprocessing | ISIC-2017 | 86.2 | 92.6 | |

BAT | ISIC-2018 | 86.3 | 92.4 | |

Model | Preprocessed | ISIC-2016 | 86.9 | 93.1 |

BA-ABC | ISIC-2017 | 87.0 | 93.0 | |

ISIC-2018 | 87.2 | 93.2 |

Algorithm | Dataset | IoU (%) | F1 (%) | |
---|---|---|---|---|

Without | ISIC-2016 | 85.8 | 92.4 | |

Preprocessing | ISIC-2017 | 89.2 | 94.3 | |

CA-Net | ISIC-2018 | 88.3 | 93.7 | |

Model | Preprocessed | ISIC-2016 | 87.0 | 93.0 |

BA-ABC | ISIC-2017 | 88.8 | 94.1 | |

ISIC-2018 | 89.7 | 94.6 |

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## Share and Cite

**MDPI and ACS Style**

Malik, S.; Akram, T.; Awais, M.; Khan, M.A.; Hadjouni, M.; Elmannai, H.; Alasiry, A.; Marzougui, M.; Tariq, U.
An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics. *Diagnostics* **2023**, *13*, 1285.
https://doi.org/10.3390/diagnostics13071285

**AMA Style**

Malik S, Akram T, Awais M, Khan MA, Hadjouni M, Elmannai H, Alasiry A, Marzougui M, Tariq U.
An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics. *Diagnostics*. 2023; 13(7):1285.
https://doi.org/10.3390/diagnostics13071285

**Chicago/Turabian Style**

Malik, Shairyar, Tallha Akram, Muhammad Awais, Muhammad Attique Khan, Myriam Hadjouni, Hela Elmannai, Areej Alasiry, Mehrez Marzougui, and Usman Tariq.
2023. "An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics" *Diagnostics* 13, no. 7: 1285.
https://doi.org/10.3390/diagnostics13071285