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Deep learning based an automated skin lesion segmentation and intelligent classification model
Journal of Ambient Intelligence and Humanized Computing ( IF 3.662 ) Pub Date : 2020-09-16 , DOI: 10.1007/s12652-020-02537-3
Mohamed Yacin Sikkandar , Bader Awadh Alrasheadi , N. B. Prakash , G. R. Hemalakshmi , A. Mohanarathinam , K. Shankar

Internet of Medical Things (IoMT) includes interconnected sensors, wearable devices, medical devices, and clinical systems. At the same time, skin cancer is a commonly available type of cancer that exists all over the globe. This study projects a new segmentation based classification model for skin lesion diagnosis by combining a GrabCut algorithm and Adaptive Neuro-Fuzzy classifier (ANFC) model. The proposed method involves four main steps: preprocessing, segmentation, feature extraction, and classification. Initially, the preprocessing step is carried out using a Top hat filter and inpainting technique. Then, the Grabcut algorithm is used to segment the preprocessed images. Next, the feature extraction process takes place by the use of a deep learning based Inception model. Finally, an adaptive neuro-fuzzy classifier (ANFC) system gets executed to classify the dermoscopic images into different classes. The proposed model is simulated using a benchmark International Skin Imaging Collaboration (ISIC) dataset and the results are examined interms of accuracy, sensitivity and specificity. The proposed model exhibits better identification and classification of skin cancer. For examining the effective outcome of the projected technique, an extensive comparison of the presented method with earlier models takes place. The experimental values indicated that the proposed method has offered a maximum sensitivity of 93.40%, specificity of 98.70% and accuracy of 97.91%.



中文翻译:

基于深度学习的自动皮肤病变分割和智能分类模型

医疗物联网(IoMT)包括互连的传感器,可穿戴设备,医疗设备和临床系统。同时,皮肤癌是全球范围内普遍使用的一种癌症。这项研究通过结合GrabCut算法和自适应神经模糊分类器(ANFC)模型,提出了一种新的基于分割的皮肤病变诊断分类模型。所提出的方法包括四个主要步骤:预处理,分割,特征提取和分类。最初,预处理步骤是使用礼帽过滤器和修复技术执行的。然后,使用Grabcut算法分割预处理图像。接下来,特征提取过程是通过使用基于深度学习的Inception模型进行的。最后,执行自适应神经模糊分类器(ANFC)系统,将皮肤镜图像分类为不同的类别。使用基准国际皮肤成像协作组织(ISIC)数据集对提出的模型进行了仿真,并对结果进行了准确性,敏感性和特异性方面的检查。提出的模型表现出更好的皮肤癌识别和分类。为了检查所预测技术的有效结果,对本文提出的方法与早期模型进行了广泛的比较。实验值表明,该方法最大灵敏度为93.40%,特异性为98.70%,准确度为97.91%。使用基准国际皮肤成像协作组织(ISIC)数据集对提出的模型进行了仿真,并对结果进行了准确性,敏感性和特异性方面的检查。提出的模型表现出更好的皮肤癌识别和分类。为了检查所预测技术的有效结果,对本文提出的方法与早期模型进行了广泛的比较。实验值表明,该方法最大灵敏度为93.40%,特异性为98.70%,准确度为97.91%。使用基准国际皮肤成像协作组织(ISIC)数据集对提出的模型进行了仿真,并对结果进行了准确性,敏感性和特异性方面的检查。提出的模型表现出更好的皮肤癌识别和分类。为了检查所预测技术的有效结果,对本文提出的方法与早期模型进行了广泛的比较。实验值表明,该方法最大灵敏度为93.40%,特异性为98.70%,准确度为97.91%。对该方法与早期模型进行了广泛的比较。实验值表明,该方法最大灵敏度为93.40%,特异性为98.70%,准确度为97.91%。对该方法与早期模型进行了广泛的比较。实验值表明,该方法最大灵敏度为93.40%,特异性为98.70%,准确度为97.91%。

更新日期:2020-09-16
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