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

Mubashar Mehmood; Sadam Hussain Abbasi; Khursheed Aurangzeb; Muhammad Faran Majeed; Muhammad Shahid Anwar; Musaed Alhussein

doi:10.3389/fonc.2023.1225490

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

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

Authors

Mubashar Mehmood¹, Sadam Hussain Abbasi², Khursheed Aurangzeb³, Muhammad Faran Majeed², Muhammad Shahid Anwar⁴, Musaed Alhussein³

Affiliations

¹ Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan.
² Department of Computer Science, Kohsar University Murree, Punjab, Pakistan.
³ Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
⁴ Department of AI and Software, Gachon University, Seongnam, Republic of Korea.

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.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This Research is funded by Researchers Supporting Project Number (RSPD2023R947), King Saud University, Riyadh, Saudi Arabia.