Timothy J Walker, Derek W Craig, Andjelka Pavlovic, Shelby Thiele, Harold W Kohl
Associations between gender, school socioeconomic status, and cardiorespiratory fitness among elementary and middle school students
BMC public health
prostate cancer common cancer significant implications global health prompt precise identification crucial efficient treatment strategizing enhanced patient results research study investigates utilization machine learning techniques diagnose prostate cancer emphasizes utilizing deep learning models namely vgg16 vgg19 resnet50 resnet50v2 extract relevant features random forest approach uses features classification study begins thorough comparison examination deep learning architectures outlined evaluate effectiveness extracting significant characteristics prostate cancer imaging data key metrics sensitivity specificity accuracy used assess models efficacy accuracy 9964 resnet50 outperformed tested models came identifying important features images prostate cancer furthermore analysis understanding factors aims offer valuable insights decisionmaking process thereby addressing critical problem clinical practice acceptance random forest classifier powerful ensemble learning method renowned adaptability ability handle intricate datasets uses collected characteristics input random forest model seeks identify patterns feature space produce precise predictions presence absence prostate cancer addition study tackles restricted availability datasets utilizing transfer learning methods refine deep learning models using small amount annotated prostate cancer data objective method improve ability models generalize across different patient populations clinical situations studys results useful show well vgg16 vgg19 resnet50 resnet50v2 work extracting features field diagnosing prostate cancer used random forests classification abilities results work provide basis creating reliable easily understandable machine learningbased diagnostic tools detecting prostate cancer enhance possibility early precise diagnosis clinical settings index terms deep learning machine learning prostate cancer cancer identification cancer classification