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Application of Deep Learning Techniques for Prostate Cancer Grading Using Histopathological Images

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1567))

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Abstract

Prostate cancer is one of the most dangerous cancers that affect men around the world. Pathologists use a variety of approaches to grade prostate cancer. Among them, microscopic examination of biopsy tissue images is the most efficient method. A timely and accurate diagnosis plays a critical role in preventing cancer from progressing. The recent achievement of deep learning (DL), notably in the convolution neural networks (CNN), is exceptional in medicine. In this study, we have investigated and compared the performance of the state-of-the-art CNN models, namely MobileNet-V2, ResNet50, DenseNet121, DenseNet169, VGG16, VGG19, Xception, InceptionV3, InceptionResNet-V2, and EfficientNet-B7 for prostate cancer grading using histopathological images. The performance of pre-trained CNNs has been evaluated on the publicly available Prostate cancer grade assessment (PANDA) dataset. On this multi-class classification problem, the EfficientNet-B7 model has achieved the highest classification accuracy of 90.90%. With such a high rate of success, the EfficientNet-B7 model may be a useful method for pathologists in determining the stage of prostate cancer.

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Authors and Affiliations

  1. Maulana Azad National Institute of Technology, Bhopal, 462003, India

    Mahesh Gour, Sweta Jain & Uma Shankar

Authors
  1. Mahesh Gour
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  2. Sweta Jain
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  3. Uma Shankar
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Corresponding author

Correspondence to Mahesh Gour .

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Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Gour, M., Jain, S., Shankar, U. (2022). Application of Deep Learning Techniques for Prostate Cancer Grading Using Histopathological Images. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_8

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  • DOI: https://doi.org/10.1007/978-3-031-11346-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11345-1

  • Online ISBN: 978-3-031-11346-8

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