Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45 rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques.

中文翻译：

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使用改进的深层和多方向不变手工特征进行皮肤病变分类


皮肤病变包括各种皮肤状况，包括由于皮肤细胞不受控制的增殖而导致的癌性生长。在全球范围内，这种疾病影响了很大一部分人口，有数百万人死亡。在过去的三十年中，皮肤癌的确诊病例不断增加，令人担忧。早期发现对于有效治疗至关重要，因为晚期诊断会显着增加死亡风险。现有的研究通常侧重于手工制作或深层特征，忽略了皮肤病变图像固有的不同纹理和结构特性。此外，基于 CNN 的方案对单个优化器的依赖带来了效率挑战。为了解决这些问题，本文提出了两种新方法，用于对皮肤镜图像中的皮肤病变进行分类，以评估癌症的严重程度。第一种方法通过利用修改后的 VGG-16 网络并采用 RMSProp 和 Adam 优化器来提高分类准确性。第二种方法引入了混合 CNN 模型，将修改后的 VGG-16 网络的深层特征与手工制作的颜色和多方向纹理特征相集成。使用基于非均匀累积概率的直方图方法提取颜色特征，而纹理特征则从基于45旋转复小波滤波器的双树复小波变换导出。合并的特征有助于准确预测皮肤病变类别。对 ISIC 2017 皮肤癌分类挑战图像的评估表明，与现有技术相比，性能显着增强。