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. 2023 Nov 9:13:1225490.
doi: 10.3389/fonc.2023.1225490. eCollection 2023.

A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI

Mubashar Mehmood  1 , Sadam Hussain Abbasi  2 , Khursheed Aurangzeb  3 , Muhammad Faran Majeed  2 , Muhammad Shahid Anwar  4 , Musaed Alhussein  3
Affiliations

A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI

Mubashar Mehmood et al. Front Oncol. .

Abstract

Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model's ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.

Keywords: MRI images; PCA; convolutional neural network; deep learning; transfer learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The top layers (last) are fine-tuned using TL.
Figure 2
Figure 2
Proposed model.
Figure 3
Figure 3
Workflow of the proposed model.
Figure 4
Figure 4
Model into work.
Figure 5
Figure 5
Efficient-net baseline model.
Figure 6
Figure 6
Accuracy curves of PCa classification.
Figure 7
Figure 7
Loss curves of PCa classification.
Figure 8
Figure 8
ROC curves of PCa.
See this image and copyright information in PMC

References

    1. Cancer society, A . American cancer society. (2021), Dataset.
    1. Şerbănescu M-S, Manea NC, Streba L, Belciug S, Ples¸ea IE, Pirici I, et al. Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks. Romanian J Morphology Embryology (2020) 61:149. doi: 10.47162/RJME.61.1.17 - DOI - PMC - PubMed
    1. Oberlin DT, Casalino DD, Miller FH, Meeks JJ. Dramatic increase in the utilization of multiparametric magnetic resonance imaging for detection and management of prostate cancer. Abdominal Radiol (2017) 42:1255–8. doi: 10.1007/s00261-016-0975-5 - DOI - PubMed
    1. Bardis MD, Houshyar R, Chang PD, Ushinsky A, Glavis-Bloom J, Chahine C, et al. Applications of artificial intelligence to prostate multiparametric MRI (mpMRI): Current and emerging trends. Cancers (2020) 12:1204. doi: 10.3390/cancers12051204 - DOI - PMC - PubMed
    1. Abbasi AA, Hussain L, Awan IA, Abbasi I, Majid A, Nadeem MSA, et al. Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cogn Neurodynamics (2020) 14:523–33. doi: 10.1007/s11571-020-09587-5 - DOI - PMC - PubMed