Abstract
Aim: The aim of the present study is to evaluate the effectiveness of computer vision-based system for the histopathological assessment of prostatic tissue and its diagnostic accuracy for the detection of hyperplasia of the glandular component of prostate.
Materials and Methods: A total of 59 digital images have been acquired from the hematoxylin and eosin-stained sections of the prostatic tissue. In these 59 images, 169 regions were marked as sites of hyperplasia of the prostatic glandular component. The entire dataset has been divided into three classes, which included the training set containing 41 images (70% of the total images), that had 109 marked regions as prostatic hyperplasia, validation set contained 6 images (10% of total images), that had 19 marked regions as prostatic hyperplasia and Testing set contained 12 images (20% of total images), that had 41 marked regions as the hyperplasia of the glandular component of the prostatic tissue.
Results: A total of fifty-nine digital images containing one hundred and sixty-nine marked regions of glandular hyperplasia of prostatic tissue are used in which 70% were employed for training, 10 % for validation, and 20% for testing. The computer vision-based system has diagnosed correctly with 96.3% f1-score.
Discussion: The application of artificial intelligence with the help of computer is emerging an important technique that will improve the diagnostic accuracy and will reduce the chance of human errors. The development of pattern recognition algorithms may be of great help in the histopathological diagnosis in the near future.
Conclusion: The present study revealed that computer vision-based system may be an effective adjunct tool for the histopathological assessment of benign prostatic hyperplasia.