Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique.

中文翻译：

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使用深度学习卷积神经网络和迁移学习方法检测前列腺癌。

男性前列腺癌已成为美国诊断最多的癌症之一，也是导致死亡的主要原因之一。由于肿块的复杂性，放射科医生无法正确检测前列腺癌。近年来，开发了许多前列腺癌检测技术，但这些技术不能有效地诊断癌症。在这项研究工作中，采用了鲁棒的深度学习卷积神经网络（CNN），使用迁移学习方法。将结果与各种机器学习策略（决策树、SVM 不同内核、贝叶斯）进行比较。癌症 MRI 数据库用于训练 GoogleNet 模型和训练机器学习分类器，提取形态学、基于熵、纹理、SIFT（尺度不变特征变换）和椭圆傅里叶描述符等各种特征。为了进行性能评估，计算各种性能测量，例如特异性、敏感性、阳性预测值、阴性预测值、假阳性率和接收操作曲线。使用迁移学习方法的 CNN 模型 (GoogleNet) 发现了最大性能。我们使用各种机器学习分类器（例如决策树、支持向量机 RBF 内核和贝叶斯）获得了相当好的结果，但是使用深度学习技术获得了出色的结果。