Application of generated mask method based on Mask R-CNN in classification and detection of melanoma. (August 2021) - Cao, Xingmei; Pan, Jeng-Shyang; Wang, Zhengdi; Sun, Zhonghai; ul Haq, Anwar; Deng, Wenyu; Yang, Shuangyuan
Highlights: A melanoma detection framework Mask-DenseNet+ is proposed based on the Mask-RCNN. It combines Mask R-CNN networks to add mask processing operations, introduces integrated learning ideas, and adjusts and improves the original DenseNet network structure. Mask-DenseNet+ is compared with some ablation experiments. The accuracy of our proposed method reaches 90.61%, the sensitivity reaches 78.00%, and the specificity reaches 93.43%. The method is feasible and effective, and achieves the preliminary goal of melanoma detection. It is greatly improved the detection accuracy and reached the level of visual diagnosis of doctors. Abstract: Objective: Melanoma is a type of malignant skin cancer with high mortality, and its incidence is increasing rapidly in recent years. At present, the best treatment is surgical resection after early diagnosis. However, due to the high visual similarity between melanoma and benign melanocytic nevus, coupled with the scarcity and imbalance of data, traditional methods are difficult to achieve good recognition and detection results. Similarly, many machine learning methods have been applied to the task of skin disease detection and classification. However, the accuracy and sensitivity of the experiments are still not satisfactory. Therefore, this paper proposed a method to identify melanoma more efficiently and accurately. Method: We implemented a Mixed Skin Lesion Picture Generate method based on Mask R-CNN (MSLP-MR) to solve the problem of data imbalance. Besides, we designed a melanoma detection framework of Mask-DenseNet+ based on MSLP-MR. This method used Mask R-CNN to introduce the method of mask segmentation, and combined with the idea of ensemble learning to integrate multiple classifiers for weighted prediction. Compared with the ablation experiments, the accuracy, sensitivity and AUC of the proposed network classification are improved by 2.56%, 29.33% and 0.0345. Result: The experimental results on the ISIC dataset shown that the accuracy of the algorithm is 90.61%, the sensitivity reaches 78.00%, which is higher than the original methods; the specificity reaches 93.43%; and the AUC reaches 0.9502. Conclusion: The method is feasible and effective, and achieves the preliminary goal of melanoma detection. It is greatly improved the detection accuracy and reached the level of visual diagnosis of doctors.