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Prostate cancer prediction from multiple pretrained computer vision model

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Abstract

The prostate gland found among men is a male reproductive gland responsible for separating a thin alkaline fluid that forms a major portion of the ejaculate. The gland has the shape of a small walnut and the cancer caused in this gland is called Prostate Cancer. It has the second highest mortality rate according to studies. Therefore, its detection at the earlier stage when it is still confined to the prostate gland is life saving. This ensures a better chance of successful treatment. The existing preliminary screening approaches for its detection includes prostate specific antigen (PSA) blood test and digital rectal exam (DRE). In the proposed method we use two popular pretrained models for feature extraction, MobileNet and DenseNet. The extracted features are stacked and augmented and fed to a two-stage classifier that provides the prediction. The proposed system is found to have an accuracy of 93.3% and outperforms other traditional approaches.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Mar Baselios College of Engineering & Technology, Nalanchira, Thiruvananthapuram, Kerala, India

    Jisha John & Aswathy Ravikumar

  2. Department of Computer Science & Engineering, College of Engineering Perumon, Kollam, Kerala, India

    Bejoy Abraham

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  1. Jisha John
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  2. Aswathy Ravikumar
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Correspondence to Jisha John.

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John, J., Ravikumar, A. & Abraham, B. Prostate cancer prediction from multiple pretrained computer vision model. Health Technol. 11, 1003–1011 (2021). https://doi.org/10.1007/s12553-021-00586-y

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