皮肤是人体暴露的部分,不断保护免受紫外线的伤害,热,光,灰尘,和其他有害辐射。影响人类的最危险的疾病之一是皮肤癌。一种称为黑色素瘤的皮肤癌始于黑素细胞,调节人类皮肤的颜色。降低皮肤癌的死亡率需要早期发现和诊断黑色素瘤等疾病。在这篇文章中,提出了一种基于自注意的周期一致性生成对抗网络,该网络采用皮肤镜图像中的黑色素瘤分类(SACCGAN-AHOA-MC-DI),并采用了Archerfish狩猎优化算法进行优化。首先,输入皮肤皮肤镜图像是通过ISIC2019的数据集收集的。然后,使用调整后的快速移位相位保持动态范围压缩(AQSP-DRC)对输入的皮肤镜图像进行预处理,以去除噪声并提高皮肤镜图像的质量。这些预处理的图像被馈送到用于ROI区域分割的分段模糊C均值聚类(PF-CMC)。分割的ROI区域被提供给十六进制局部自适应二进制模式(HLABP)以提取放射学特征,如灰度统计特征(标准偏差,意思是,峰度,和偏度)与Haralick纹理特征(对比,能源,熵,同质性,和相反的不同时刻)。提取的特征被馈送到基于自我注意力的周期一致性生成对抗网络(SACCGAN),该网络将皮肤癌分类为黑素细胞痣,基底细胞癌,光化性角化病,良性角化病,皮肤纤维瘤,血管病变,鳞状细胞癌和黑色素瘤。总的来说,SACCGAN不适应任何优化模式来定义理想的参数,以确保皮肤癌的准确分类。因此,archerfish狩猎优化算法(AHOA)被认为是最大化SACCGAN分类器,对皮肤癌进行了准确的分类。所提出的方法达到23.01%,14.96%,和45.31%的准确度和32.16%,11.32%,与现有方法相比,计算时间减少了24.56%,如通过秃鹰搜索优化(CNN-BES-MC-DI)利用优化的挤压网络对不平衡数据的黑色素瘤预测方法,基于灰狼优化算法的超参数优化CNN(CNN-GWOA-MC-DI),DEANN根据模糊c均值聚类(DEANN-MC-DI)激发了皮肤癌的发现。研究重点:这份手稿,基于自我注意的周期一致性。SACCGAN-AHOA-MC-DI方法在Python中实现。提出了来自皮肤镜图像的(SACCGAN-AHOA-MC-DI)。调整快速移位相位保持动态范围压缩(AQSP-DRC)。去除噪声并提高皮肤皮肤镜图像的质量。
Skin is the exposed part of the human body that constantly protected from UV rays, heat, light, dust, and other hazardous radiation. One of the most dangerous illnesses that affect people is skin cancer. A type of skin cancer called melanoma starts in the melanocytes, which regulate the colour in human skin. Reducing the fatality rate from skin cancer requires early detection and diagnosis of conditions like melanoma. In this article, a Self-attention based cycle-consistent generative adversarial network optimized with Archerfish Hunting Optimization Algorithm adopted Melanoma Classification (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Primarily, the input Skin dermoscopic images are gathered via the dataset of ISIC 2019. Then, the input Skin dermoscopic images is pre-processed using adjusted quick shift phase preserving dynamic range compression (AQSP-DRC) for removing noise and increase the quality of Skin dermoscopic images. These pre-processed images are fed to the piecewise fuzzy C-means clustering (PF-CMC) for ROI region segmentation. The segmented ROI region is supplied to the Hexadecimal Local Adaptive Binary Pattern (HLABP) to extract the Radiomic features, like Grayscale statistic features (standard deviation, mean, kurtosis, and skewness) together with Haralick Texture features (contrast, energy, entropy, homogeneity, and inverse different moments). The extracted features are fed to self-attention based cycle-consistent generative adversarial network (SACCGAN) which classifies the skin cancers as Melanocytic nevus, Basal cell carcinoma, Actinic Keratosis, Benign keratosis, Dermatofibroma, Vascular lesion, Squamous cell carcinoma and melanoma. In general, SACCGAN not adapt any optimization modes to define the ideal parameters to assure accurate classification of skin cancer. Hence, Archerfish Hunting Optimization Algorithm (AHOA) is considered to maximize the SACCGAN classifier, which categorizes the skin cancer accurately. The proposed method attains 23.01%, 14.96%, and 45.31% higher accuracy and 32.16%, 11.32%, and 24.56% lesser computational time evaluated to the existing methods, like melanoma prediction method for unbalanced data utilizing optimized Squeeze Net through bald eagle search optimization (CNN-BES-MC-DI), hyper-parameter optimized CNN depending on Grey wolf optimization algorithm (CNN-GWOA-MC-DI), DEANN incited skin cancer finding depending on fuzzy c-means clustering (DEANN-MC-DI). RESEARCH HIGHLIGHTS: This manuscript, self-attention based cycle-consistent. SACCGAN-AHOA-MC-DI method is implemented in Python. (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Adjusted quick shift phase preserving dynamic range compression (AQSP-DRC). Removing noise and increase the quality of Skin dermoscopic images.