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首页> 外文期刊>Journal of Cancer Research and Clinical Oncology >Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm
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Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm

机译：计算机辅助医学图像分类，用于利用深层学习算法的口腔癌早期诊断

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PurposeOral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification are made possible. Medical imaging technique, computer-aided diagnosis and detection can make potential changes in cancer treatment. In this research work, we have developed a deep learning algorithm for automated, computer-aided oral cancer detecting system by investigating patient hyperspectral images.MethodsTo validate the proposed regression-based partitioned deep learning algorithm, we compare the performance with other techniques by its classification accuracy, specificity, and sensitivity. For the accurate medical image classification objective, we demonstrate a new structure of partitioned deep Convolution Neural Network (CNN) with two partitioned layers for labeling and classify by labeling region of interest in multidimensional hyperspectral image.ResultsThe performance of the partitioned deep CNN was verified by classification accuracy. We have obtained classification accuracy of 91.4% with sensitivity 0.94 and a specificity of 0.91 for 100 image data sets training for task classification of cancerous tumor with benign and for task classification of cancerous tumor with normal tissue accuracy of 94.5% for 500 training patterns was obtained.ConclusionsWe compared the obtained results from another traditional medical image classification algorithm. From the obtained result, we identify that the quality of diagnosis is increased by proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis.
机译：目的癌症是复杂的广泛蔓延癌症，其严重程度高。使用先进的技术和深度学习算法，提前检测和分类是可能的。医学成像技术，计算机辅助诊断和检测可以对癌症治疗产生潜在的变化。在本研究工作中，我们通过调查患者高光谱图像开发了一种自动化的计算机辅助口腔癌检测系统的深度学习算法。方法验证了所提出的回归的分区深度学习算法，我们通过其分类将性能与其他技术进行比较准确性，特异性和敏感性。对于准确的医学图像分类目标，我们展示了一个具有两个分区层的分区深度卷积神经网络（CNN）的新结构，用于通过在多维高光谱图像中标记感兴趣的兴趣区域来标记和分类。验证了分区深CNN的性能。分类准确性。我们已经获得了91.4％的敏感性0.94的分类准确性，100个图像数据集癌症肿瘤任务分类的培训表达0.91，获得了癌症肿瘤的任务分类，并获得了500种训练模式的正常组织精度为94.5％与另一种传统的医学图像分类算法相比，与另一种传统的医学图像分类算法的结果进行了比较。从获得的结果中，我们确定通过提出基于回归的分区CNN学习算法来增加诊断质量，用于口腔癌诊断的复杂医学图像。

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