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Photoacoustic imaging (PAI), also known as optoacoustic imaging, is a rapidly growing imaging modality with potential in medical diagnosis and therapy monitoring. This paper focuses on the techniques of prostate PAI and its potential applications in prostate cancer detection. Transurethral light delivery combined with transrectal ultrasound detection overcomes light scattering in the surrounding tissue and provides optimal photoacoustic signals while minimizing invasiveness. While label-free PAI based on endogenous contrast has promising potential for prostate cancer detection, exogenous contrast agents can further enhance the sensitivity and specificity of prostate cancer PAI. Further in vivo studies are required in order to achieve the translation of prostate PAI to clinical implementation. The minimal invasiveness, relatively low cost, high specificity and sensitivity, and real-time imaging capability are valuable advantages of PAI that may improve the current prostate cancer management in clinic.

Prostate cancer is increasingly common among men. However, the process of diagnosing malignant disease is relatively complicated and time-consuming. Identifying benign or malignant tumors early can assist medical professionals in choosing appropriate treatment methods. Consequently, we introduce a soft-voting ensemble model comprising several single machine learning models such as Logistic Regression, Random Forest, XGBoost, LGBM, and Support Vector Machine for the classification task with the prostate cancer dataset. The dataset was divided into two parts for training and testing with a ratio of 67:33. The confusion matrix was used to evaluate the performance of both the individual and ensemble models. Experimental results show that ensemble models achieve performance ranging from 87.88% to 96.97%, which is 3% to 9% better than individual models, surpassing recent research. Integrating the strengths of individual models helps minimize errors, resulting in optimal classification with high accuracy and overall performance in the field of machine learning.