Gannet devil optimization-based deep learning for skin lesion segmentation and identification

Skin lesion identification is a way of identifying abnormal skin tissues from normal skin. The affected area of the skin are segmented in the skin lesion segmentation process, which helps to improve the identification task. In this paper, the skin lesion segmentation and identification process are completed through efficient deep-learning networks. Here, the Wiener filter is employed for preprocessing and is used to cleans up the noise present in the image. The UNet++ model completes the skin lesion segmentation and it separates the affected region from the noise-free images. By considering the segmented portion, the prevalent features namely Convolutional Neural Network (CNN) features, Local Optimal Oriented Pattern (LOOP), statistical features, Gray-Level Co-Occurrence Matrix (GLCM) features, Local ternary pattern (LTP), and Shape Local Binary Texture (SLBT) features are mined out. Moreover, the LeNet model completes the skin lesion segmentation and the weight of both UNet++ and LeNet is tuned by the devised Gannet Devil Optimization Algorithm (GDO), which is the incorporation of Gannet Optimization Algorithm (GOA) and Tasmanian Devil Optimization algorithm (TDO). To segment and classify the lesions from the medical images, the dataset SIIM-ISIC Melanoma Identification data is taken into consideration. Besides, the experimental result indicates the GDO-LeNet provided improved performance regarding the accuracy, True Positive Rate (TPR), True Negative Rate (TNR), positive predictive value (PPV), and Negative predictive value (NPV) of 0.924, 0.925, 0.937, 0.896 and 0.890.

Automated summary

This study utilized deep learning networks for skin lesion identification and segmentation, incorporating various features mining and model weight tuning to improve performance metrics such as accuracy.