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Improving classification accuracy for prostate cancer using noise removal filter and deep learning technique

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Abstract

Prostate Cancer (PCa) can be considered as the second cause of death among men all over the world. Different techniques based on deep learning have been proposed for accurate PCa detection using Magnetic Resonance Imaging (MRI) images. In this research work, an accurate 2d CNN-based Convolutional Neural Network (CNN) model is developed and implemented for PCa binary classification (0 for Benign and 1 for Malignant).The paper is aimed at improving the classification accuracy in two phases. The first one is to use image pre-processing algorithms such as (DICOM to jpg format, image resizing and labeling, adding noise, and noise removal by median filter). The second improvement is achieved by increasing the dataset size. The dataset of 20 patients were used which consist of 15 patients (3249 MRI slices) with cancerous tumor and 3 patients without cancer (1751 MRI slices). To evaluate the performance accuracy of the proposed approach, 30% of the dataset is used for the test and validation while 70% used for the training. The accuracy is found based on the Area under Curve (AUC) of Receiver Operating Characteristic (ROC). Test results indicate that the AUC is 0.98 without pre-processing whereas its value increased to 0.9993 with pre-processing using epoch iteration = 60 and the total dataset images.

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Authors and Affiliations

  1. Department of Information Technology, Technical College of Informatics, Sulaimani Polytechnic University, KRG, Sulaimani, Iraq

    Ari M. Ali

  2. Computer Science Department, College of Science, University of Sulaimani, Sulaimani, KRG, Iraq

    Aree A. Mohammed

  3. Computer Science Department, College of Science, University of Halabja, Halabja, KRG, Iraq

    Aree A. Mohammed

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  1. Ari M. Ali
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Correspondence to Aree A. Mohammed.

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Ali, A.M., Mohammed, A.A. Improving classification accuracy for prostate cancer using noise removal filter and deep learning technique. Multimed Tools Appl 81, 8653–8669 (2022). https://doi.org/10.1007/s11042-022-12102-z

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  • DOI: https://doi.org/10.1007/s11042-022-12102-z

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