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 computer-aided 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.

Khaja Raoufuddin Ahmed, Siti Zura A Jalil and Sahnius Usman. “Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.5 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140595

@article{Ahmed2023,
title = {Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140595},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140595},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {5},
author = {Khaja Raoufuddin Ahmed and Siti Zura A Jalil and Sahnius Usman}
}