Abstract
Prostate cancer is solid organ melanoma which increases mortality amongst humans. Automatic techniques for determining prostate cancer from magnetic resonance images (MRI) are highly recommended. Conventional techniques adapt different steps, which may result in huge computational costs. In order to perform automated prostate cancer classification with MRI, a deep model named bird squirrel (BS) algorithm-based deep recurrent neural networks (DeepRNN) is developed in this research. Here, the MRI noise is removed using a non-local means (NLM) filter. Convolution neural networks (CNN) are also widely used to create segments in order to extract notable features, and they are used in DeepRNN for detecting prostate cancer. To train the classifier, the proposed BS algorithm is used. By combining the bird search algorithm (BSA) and squirrel search algorithm (SSA), the created BS is produced. The evaluation is done using Prostate MRI Dataset and the invented BS-DeepRNN is obtained with a higher accuracy of 0.937, a sensitivity of 0.958, and a specificity of 0.916.
Published Version
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