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Deep Learning for Detection of Prostate Tumors by Microscopic Cells and MRI

The most common type of cancer in men is prostate cancer. Since it initially grows slowly at the level of the prostate, it does not cause any major damage. However, some types of prostate cancer are not dangerous because they grow slowly and require little or no treatment. In contrast, other types of prostate cancer are aggressive and can spread quickly. This paper studies the effectiveness of Local Binary Patterns (LBP) method, skewness coefficients for asymmetry detection and correction, morphological transformations, and image classification using Convolutional Neural networks (CNNs), to detect prostate cancer tumor from microscopic cells and MRI imaging. The experimental results carried out have shown the effectiveness of the proposed methods using deep learning architectures.