Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers,Microscopy Research and Technique

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Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers
Microscopy Research and Technique ( IF 2.0 ) Pub Date : 2022-07-25 , DOI: 10.1002/jemt.24211
Jaber Alyami _{1,

2,

3} , Amjad Rehman ₄ , Tariq Sadad ₅ , Maryam Alruwaythi ₄ , Tanzila Saba ₄ , Saeed Ali Bahaj ₆

Affiliation

Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results. Thus, the automated system is required to assist physicians in diagnosing skin cancer at early stage precisely to decrease the mortality rate. This article presents an automatic skin lesions detection through a microscopic hybrid feature set and machine learning-based classification. The employment of deep features through AlexNet architecture with local optimal-oriented pattern can accurately predict skin lesions. The proposed model is tested on two open-access datasets PAD-UFES-20 and MED-NODE comprising melanoma and nevus images. Experimental results on both datasets exhibit the efficacy of hybrid features with the help of machine learning. Finally, the proposed model achieved 94.7% accuracy using an ensemble classifier.

中文翻译：

通过微观混合特征集和机器学习分类器从图像中自动检测皮肤病变

由于大量人群和皮肤专家缺乏认识，全世界皮肤癌的发病率呈指数级增长。医学成像可以帮助早期发现和更准确地诊断皮肤癌。医生在诊所中通常遵循手动诊断方法，但非专业皮肤科医生有时会影响结果的准确性。因此，需要自动化系统来协助医生在早期准确诊断皮肤癌，以降低死亡率。本文提出了一种通过微观混合特征集和基于机器学习的分类来自动检测皮肤病变的方法。通过具有局部最优导向模式的 AlexNet 架构使用深层特征可以准确预测皮肤病变。所提出的模型在两个包含黑色素瘤和痣图像的开放数据集 PAD-UFES-20 和 MED-NODE 上进行了测试。两个数据集的实验结果在机器学习的帮助下展示了混合特征的功效。最后，所提出的模型使用集成分类器实现了 94.7% 的准确率。

更新日期：2022-07-25

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