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Prostate classification network (PC-Net) for automated classification of Prostate cancer in Magnetic resonance imaging

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Abstract

Prostate cancer (PCa) is found to be the second most common cause of death in men after lung cancer, making it necessary to diagnose as early as possible. The major modality used for PCa detection is Magnetic resonance imaging (MRI), as it is acquired in a radiation-free area, making its visibility better. So, developing MRI-based Computer-aided diagnosis (CAD) systems for PCa is becoming one of the most dynamic areas of research these days. The traditional approaches used to examine PCa are mainly manual and consume much time. CAD system thus reduces the manual approach employing different image processing approaches, thereby increasing the accuracy of PCa diagnosis. This paper presents a deep learning-based methodology named Prostate Classification Network (PC-Net) for the classification of cancer from the T2-weighted (T2w) MRI modality. The proposed model gave an accuracy of 97.12%, sensitivity of 98.18% and specificity of 95.67%.

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Funding

The authors are grateful to the Ministry of Human Resource Development (MHRD), Govt. of India for funding this project(17–11/2015-PN-1) under Design Innovation Centre (DIC) sub-theme Medical Devices & Restorative Technologies.

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  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Mamta Juneja, Sumindar Kaur Saini, Kunal Sharma & Prashant Jindal

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  1. Mamta Juneja
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Correspondence to Prashant Jindal.

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Juneja, M., Saini, S.K., Sharma, K. et al. Prostate classification network (PC-Net) for automated classification of Prostate cancer in Magnetic resonance imaging. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19177-w

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  • DOI: https://doi.org/10.1007/s11042-024-19177-w

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