Malignant melanoma is considered to be one of the deadliest types of skin cancers which is responsible for the massive number of deaths worldwide. According to the American Cancer Society (ACS), more than a million Americans are living with this melanoma. Since 2019, 192,310 new cases of melanoma are registered, where 95,380 are noninvasive, and 96,480 are invasive. The numbers of deaths due to melanoma in 2019 alone are 7,230, comprising 4,740 men and 2,490 women. Melanoma may be curable if diagnosed at the earlier stages; however, the manual diagnosis is time-consuming and also dependent on the expert dermatologist. In this work, a fully automated computerized aided diagnosis (CAD) system is proposed based on the deep learning framework. In the proposed scheme, the original dermoscopic images are initially pre-processed using the decorrelation formulation technique, which later passes the resultant images to the MASK-RCNN for the lesion segmentation. In this step, the MASK RCNN model is trained using the segmented RGB images generated from the ground truth images of ISBI2016 and ISIC2017 datasets. The resultant segmented images are later passed to the DenseNet deep model for feature extraction. Two different layers, average pool and fully connected, are used for feature extraction, which are later combined, and the resultant vector is forwarded to the feature selection block for down - sampling using proposed entropy-controlled least square SVM (LS-SVM). Three datasets are utilized for validation - ISBI2016, ISBI2017, and HAM10000 to achieve an accuracy of 96.3%, 94.8%, and 88.5% respectively. Further, the performance of MASK-RCNN is also validated on ISBI2016 and ISBI2017 to attain an accuracy of 93.6% and 92.7%. To further increase our confidence in the proposed framework, a fair comparison with other state-of-the-art is also provided.

恶性黑色素瘤被认为是最致命的皮肤癌类型之一，是导致全球大量死亡的原因。根据美国癌症协会（ACS）的调查，超过一百万的美国人患有这种黑色素瘤。自2019年以来，登记了192,310例新的黑色素瘤病例，其中95,380例为非侵入性，而96,480例为侵入性。仅在2019年，由于黑色素瘤导致的死亡人数为7,230，其中包括4,740名男性和2,490名女性。如果在早期阶段被诊断出黑色素瘤可以治愈。但是，手动诊断非常耗时，而且还取决于专业的皮肤科医生。在这项工作中，基于深度学习框架，提出了一个全自动计算机辅助诊断（CAD）系统。在建议的方案中，最初，使用去相关公式化技术对原始的皮肤镜图像进行预处理，然后再将所得图像传递给MASK-RCNN进行病变分割。在此步骤中，使用从ISBI2016和ISIC2017数据集的地面真实图像生成的分段RGB图像训练MASK RCNN模型。生成的分割图像随后被传递到DenseNet深度模型以进行特征提取。使用两个不同的层（平均池和完全连接层）进行特征提取，然后将其组合，然后将所得向量转发到特征选择块，以使用建议的熵控制的最小二乘SVM（LS-SVM）进行下采样。使用三个数据集进行验证-ISBI2016，ISBI2017和HAM10000，分别达到96.3％，94.8％和88.5％的准确性。进一步，MASK-RCNN的性能也在ISBI2016和ISBI2017上得到了验证，准确度分别为93.6％和92.7％。为了进一步提高我们对拟议框架的信心，还与其他最新技术进行了公平比较。