A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification.
A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification.
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DOI：
10.1155/2021/9619079
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发表时间：
2021
影响因子：
--
通讯作者：
Kadry S
中科院分区：
文献类型：
--
作者：
Arshad M;Khan MA;Tariq U;Armghan A;Alenezi F;Younus Javed M;Aslam SM;Kadry S
在美国，每年有近 540 万人被诊断患有皮肤癌。黑色素瘤是最危险的皮肤癌之一，其生存率为 5%。在过去的几年里，皮肤癌的发病率有所上升。早期识别皮肤癌有助于降低人类死亡率。皮肤镜检查是一种用于采集皮肤图像的技术。然而，人工检查过程耗时较长，成本较高。深度学习领域的最新发展显示出分类任务的显着性能。在这项研究工作中，提出了一种用于多类皮肤病变分类的新自动化框架。拟议的框架由一系列步骤组成。第一步，进行增强。对于增强过程，执行三个操作：旋转90、左右翻转和上下翻转。第二步，对深度模型进行微调。选择了两个模型，例如ResNet-50和ResNet-101，并更新了它们的层。第三步，应用迁移学习在增强数据集上训练两个微调的深度模型。在后续阶段，使用改进的基于串行的方法提取特征并执行融合。最后，通过使用偏度控制的 SVR 方法选择最佳特征，进一步增强融合向量。最终选择的特征使用多种机器学习算法进行分类，并根据准确度值进行选择。在实验过程中，使用增强的HAM10000数据集，取得了91.7%的准确率。此外，与原始不平衡数据集相比，增强数据集的性能更好。此外，所提出的方法与最近的一些研究进行了比较，并显示出改进的性能。
In the USA, each year, almost 5.4 million people are diagnosed with skin cancer. Melanoma is one of the most dangerous types of skin cancer, and its survival rate is 5%. The development of skin cancer has risen over the last couple of years. Early identification of skin cancer can help reduce the human mortality rate. Dermoscopy is a technology used for the acquisition of skin images. However, the manual inspection process consumes more time and required much cost. The recent development in the area of deep learning showed significant performance for classification tasks. In this research work, a new automated framework is proposed for multiclass skin lesion classification. The proposed framework consists of a series of steps. In the first step, augmentation is performed. For the augmentation process, three operations are performed: rotate 90, right-left flip, and up and down flip. In the second step, deep models are fine-tuned. Two models are opted, such as ResNet-50 and ResNet-101, and updated their layers. In the third step, transfer learning is applied to train both fine-tuned deep models on augmented datasets. In the succeeding stage, features are extracted and performed fusion using a modified serial-based approach. Finally, the fused vector is further enhanced by selecting the best features using the skewness-controlled SVR approach. The final selected features are classified using several machine learning algorithms and selected based on the accuracy value. In the experimental process, the augmented HAM10000 dataset is used and achieved an accuracy of 91.7%. Moreover, the performance of the augmented dataset is better as compared to the original imbalanced dataset. In addition, the proposed method is compared with some recent studies and shows improved performance.
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DOI：
10.1186/s13673-020-00216-y
发表时间：
2020-03-31
影响因子：
6.6
作者：
Akram, Tallha;Lodhi, Hafiz M. Junaid;Qadri, Nadia N.
通讯作者：
Qadri, Nadia N.
影响因子：
3.9
作者：
Hammad, Mohamed;Alkinani, Monagi H.;Abd El-Latif, Ahmed A.
通讯作者：
Abd El-Latif, Ahmed A.
影响因子：
3.6
作者：
Melbin, K.;Raj, Y. Jacob Vetha
通讯作者：
Raj, Y. Jacob Vetha
影响因子：
6
作者：
Meraj, Talha;Raul, Hafiz Tayyab;Shoaib, Umar
通讯作者：
Shoaib, Umar
影响因子：
3.6
作者：
Khan, Muhammad Attique;Ashraf, Imran;Bukhari, Syed Ahmad Chan
通讯作者：
Bukhari, Syed Ahmad Chan