基于改进金枪鱼群的U-EfficientNet:基于改进金枪鱼群优化的皮肤病变图像分割

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS
Khaja Raoufuddin Ahmed, S. A. Jalil, S. Usman
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引用次数: 1

摘要

——皮肤癌呈上升趋势,其中最严重的是黑色素瘤。越来越多的调查机构正在使用数码相机图像来计算机辅助检查可疑的皮肤病变是否患有癌症。由于存在干扰因素,包括光照波动和表面光反射,这些图像的解释通常是困难的。从健康皮肤中分割病变区域是诊断癌症的关键一步。因此,本研究引入了一种优化的深度学习方法来分割皮肤病变。为此,effentnet与UNet集成,以提高分割精度。同时,利用改进的金枪鱼群优化算法(ITSO)对U-EfficientNet的可修改参数进行调整,使学习过程中的信息损失最小化。根据准确度(Accuracy)、均方误差(MSE)、精密度(Precision)、召回率(Recall)、IoU和骰子系数(Dice Coefficient)等多种评价指标对所提出的itsu - effentnet进行了评估,得到的值分别为0.94、0.06、0.94、0.94、0.92和0.94。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization
—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.
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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