Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization

Abstract

Skin cancers have been on an upward trend, with melanoma being the most severe type. A growing body of investigation is employing digital camera images to computeraided examine suspected skin lesions for cancer. Due to the presence of distracting elements including lighting fluctuations and surface light reflections, interpretation of these images is typically difficult. Segmenting the area of the lesion from healthy skin is a crucial step in the diagnosis of cancer. Hence, in this research an optimized deep learning approach is introduced for the skin lesion segmentation. For this, the EfficientNet is integrated with the UNet for enhancing the segmentation accuracy. Also, the Improved Tuna Swarm Optimization (ITSO) is utilized for adjusting the modifiable parameters of the U-EfficientNet to minimize the information loss during the learning phase. The proposed ITSU-EfficientNet is assessed based on various evaluation measures like Accuracy, Mean Square Error (MSE), Precision, Recall, IoU, and Dice Coefficient and acquired the values are 0.94, 0.06, 0.94, 0.94, 0.92 and 0.94 respectively.

A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images

“…Distinct from conventional models, this approach deploys parallel sub-models to analyze two-channel data subsets independently, thereby optimizing learning processes and discerning complex data correlations. The incorporation of a distinctive batch normalization technique contributes to model stability, augmenting resilience for tasks involving high-resolution criteria [33]. The amalgamation of these varied feature sets enables Ef-ficientNet B7 to provide an exhaustive and subtle interpretation of multichannel data, establishing it as a superior solution for enhancing the accuracy of high-resolution wetland image segmentation [34].…”