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Investigations of Shallow and Deep Learning Algorithms for Tumor Detection

机译：浅层和深度学习算法在肿瘤检测中的研究

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With the increasing ability of the computer aided detection and diagnosis system, the exploration on the tumors have attained a breakthrough by decreasing the mortality rate. Radiologists perform manual identification for both the diagnosis and prognosis of tumors in the victims. The research on the diagnosis of the tumor have reached a huge milestone using both the shallow and the deep learning algorithms. In this paper, classification of mass lesions in breast cancer dataset has been performed, for studying the comparison of shallow learning algorithms with that of the deep learning algorithms. For shallow learning the texture and statistical features were extracted, used in the contemporary classification algorithms. Of all shallow learning algorithms, support vector machines (SVM) showed 90.6% accuracy, whereas among the deep learning algorithms, the global features are taken for consideration and VGG16 showed 92.6% accuracy. Thus, these deep learning models outperformed the shallow algorithms and have been a state-of-art algorithm for an accurate and automated breast tumor detection.
机译：随着计算机辅助检测与诊断系统能力的增强，对肿瘤的探索已通过降低死亡率来取得突破。放射科医师对受害人的肿瘤进行诊断和预后识别。使用浅层和深度学习算法，对肿瘤诊断的研究已达到了一个巨大的里程碑。在本文中，对乳腺癌数据集中的大块病变进行了分类，以研究浅层学习算法与深层学习算法的比较。为了进行浅层学习，提取了纹理和统计特征，并将其用于当代分类算法中。在所有浅层学习算法中，支持向量机（SVM）的准确性为90.6％，而在深度学习算法中，考虑了全局特征，而VGG16的准确性为92.6％。因此，这些深度学习模型的性能优于浅层算法，并且已成为用于准确，自动进行乳腺肿瘤检测的最新算法。

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