Document Type : Full Research Paper

Authors

• Amir Sezavar ¹
• Hassan Farsi ²
• Farima Farsi ³

¹ Ph.D Student, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

² Professor, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

³ Medical Doctoral Student, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Prostate cancer is one of the most important diseases of men whose growth can be disrupted by early diagnosis of it. In order to determine the grade of prostate cancer, the biopsy is used and structure of tissue is examined under microscopes. According to new grading system, the prostate tissues are grading to five categories, between 1 to 5, where the highest grade shows the worst condition. Since human grading is time consuming, automatic grading systems have been used since recent years. Although some efficient algorithms have been introduced for image classification, the semantic gap between low-level features and human visual concept is still an important reason not to achieve high precision. In this paper, a new method for prostate cancer grading is presented which uses a combination of deep features, extracted by convolional neural network (CNN), and stochastic tissue features, extracted using multi-level gray level co-occurrence matrixes (ML-GLCM). Therefore, high-level features are achieved by using CNN and by combining with stochastic tissue features, the grading precision is increased. In order to evaluate the proposed method, it is examined on the pathology prostate image database which is generated by international society of urological pathology (ISUP). Experimental results demonstrate that the proposed method achieves more accuracy than state-of-the-art methods on prostate cancer grading.

Keywords

• Prostate Cancer
• Grading
• Pathological Images
• Stochastic Tissue Features
• Convolutional Neural Network (CNN)

Main Subjects

• Biomedical Image Processing / Medical Image Processing