ty -jour a2 -abd el -latif，ahmed A. au -arshad，mehak au -khan，穆罕默德·阿特克（Muhammad Attique au -tariq），塔里克（Tariq），乌斯曼·阿姆（Usman au -usman au） - 阿姆根（Ammar Au -Ammar au -ammar au -ammar au -ammar au -alenezi -alenezi，fayadh au- younus javed javed- younus javed）Mohamed Au -Kadry，塞法丁PY -2021 DA -2021/12/06 Ti-一种使用深度学习的计算机辅助诊断系统，用于多类皮肤病变分类SP -9619079 VL -2021 AB-每年，近5400万人们被诊断出患有皮肤癌。黑色素瘤是皮肤癌最危险的类型之一，其存活率为5％。在过去的几年中，皮肤癌的发展一直在增长。皮肤癌的早期鉴定可以帮助降低人类死亡率。皮肤镜检查是一种用于获取皮肤图像的技术。但是，手动检查过程会消耗更多的时间，并且需要花费大量费用。深度学习领域的最新发展显示了分类任务的显着性能。在这项研究工作中，提出了一个新的自动化框架，用于多类皮肤病变分类。 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. SN - 1687-5265 UR - https://doi.org/10.1155/2021/9619079 DO - 10.1155/2021/9619079 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -