Deep learning methods have gained significant traction in various fields, including medicine, natural sciences, computer sciences, technical sciences, and life sciences [1–4]. Over the past decade, deep learning approaches have been successfully applied in a wide array of fields, such as computed tomography (CT) [5–7], magnetic resonance imaging (MRI) [9–10], digital radiography (DR) [10–12], positron emission tomography (PET) [13–17], and ultrasound tomography [18–20]. Given their widespread success, deep learning methods have been considered for classifying tasks within ultrasound images.
One area where deep learning techniques have demonstrated promise is in the diagnosis and management of conditions that predominantly affect the aging male population. As life expectancy continues to increase globally, there is a growing need for accurate diagnostic tools and effective treatments for age-related health issues. Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are two such conditions that are highly prevalent among aging males [21]. The early detection of PCa or BPH is crucial for maintaining health and ensuring accurate diagnoses, as timely intervention can significantly improve patient outcomes.
Magnetic resonance imaging (MRI), digital rectal examination (DRE), and transrectal ultrasound (TRUS) are common clinical tools for studying PCa and BPH [23–25]. Among these, TRUS offers several advantages, including no radiation exposure, ease of operation, and real-time scanning. However, false-positive and negative rates hinder diagnostic accuracy due to spike noise, depth attenuation effects, and scattering phenomena between media [26–32]. In light of these challenges, there is considerable interest in exploring the potential of deep learning techniques for improving the diagnostic process.
Deep learning approaches have recently been applied to classify BPH and PCa using MRI. While TRUS remains a popular imaging tool for prostate clinical diagnostic studies, the classification of PCa and BPH using deep learning methods warrants further investigation [33, 34]. Some studies have applied deep learning methods to diagnose prostate cancer using B-mode ultrasonography and sonoelastography [33, 34]. B-mode ultrasound is potentially suitable for PCa imaging due to its real-time capabilities. As a result, artificial intelligence with deep learning schema has been employed to improve diagnostic accuracy in PCa via B-mode ultrasound (TRUS) [33, 34]. Furthermore, machine learning approaches with feature-based techniques continue to progress, enhancing classification performance for PCa [33, 34].
The application of deep learning techniques in the medical field is particularly promising due to the large volumes of data generated by modern imaging techniques. By harnessing the power of big data, researchers can develop more sophisticated algorithms capable of identifying subtle patterns and correlations that might otherwise be missed by conventional diagnostic methods. As such, the use of deep learning methods has the potential to revolutionize the way we approach the diagnosis and management of age-related health issues in the aging male population
In this context, there is a pressing need for studies that systematically evaluate the performance of deep learning methods in classifying PCa and BPH using ultrasound images. Such investigations can provide valuable insights into the strengths and limitations of various techniques and help guide the development of more effective diagnostic tools. Additionally, by comparing the performance of deep learning methods with that of traditional machine learning techniques, researchers can gain a better understanding of the unique contributions that each approach brings to the table.
The primary objective of this study is to investigate and compare the classification performance of PCa and BPH using B-mode ultrasound, based on popular deep learning methods with transferred learning. By exploring the potential of these advanced techniques, we aim to contribute to the ongoing efforts to improve diagnostic accuracy and early detection for both PCa and BPH, ultimately leading to better health outcomes for the aging male population.