基于概率多层密集网络的无人迁移学习皮肤癌检测与分类

V. Nyemeesha, M. Kavitha, B. M. Ismail
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引用次数: 1

摘要

皮肤癌是最危险的癌症之一,可能发生在不同年龄组的人身上。因此,皮肤癌的早期识别有可能挽救数百万人的生命。在传统的机器学习方法中,在皮肤病变的检测和分类方面存在各种缺陷。因此,为了实现鲁棒性,首先采用基于卷积自动编码器和解码器(CAED)的预处理方法联合三边和双边滤波器(JTBF)来增强皮肤病变,同时去除病变部位的毛发。然后,将基于迁移学习的概率多层密集网络(PMDN)方法的无人迁移学习分割方法应用于准确检测皮肤病变的癌变区域。进一步,利用迁移学习卷积神经网络(TL-CNN)对分割区域进行特征提取,提取出详细的inter-disease dependent (IDD)和intra-disease specific (IDS)特征。最后,对Alexa Net模型进行IDD、IDS特征的训练和测试,并对八种不同的皮肤癌类型进行分类。利用Adam优化器对迁移学习网络的复杂度进行了优化。最后,仿真结果表明,与传统方法相比,该模型具有更好的分割、特征提取和分类性能。在ISIC-2019公共挑战数据集上,该方法实现了99.937%的分割准确率、99.47%的特征提取准确率和99.27%的分类准确率。
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Detection and Classification of Skin Cancer Using Unmanned Transfer Learning Based Probabilistic Multi-Layer Dense Networks
Skin cancer is one of the most dangerous cancers that may occur for different age groups of people. As a result, early identification of skin cancer has the potential to save millions of lives. In Traditional machine learning approaches, there are various drawbacks in detection and classification of skin lesions. As a result, to achieve the robust performance, initially the joint trilateral and bilateral filter (JTBF) with convolutional auto encoder and decoder (CAED)-based preprocessing method is used to enhance the skin lesion and also removes hair from lesions. Then, transfer learning-based probabilistic multi-layer dense networks (PMDN) method-based unmanned Transfer learning segmentation method is adapted for accurately detecting the cancer region on skin lesions. Further, transfer learning convolution neural network (TL-CNN) is used to extract the features from the segmented region, which extracts the detailed inter-disease-dependent (IDD) and intra-disease specific (IDS) features. Finally, Alexa Net model is trained and tested with the IDD, IDS features and classifies the eight different skin cancer types. The complexity of the transfer learning networks is optimized by the using the Adam optimizer. Finally, the simulation results show that the proposed model resulted in superior segmentation, feature extraction, and classification performances as compared to conventional approaches. Further, the proposed method achieved 99.937% segmentation accuracy, 99.47% feature extraction accuracy, and 99.27% classification accuracy on ISIC-2019 public challenge dataset.
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