Prostate Cancer Grading and Classification by Combining Deep Features and Stochastic Tissue Features of Pathological Prostate Images

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Sezavar Amir; FARSI HASSAN; Farsi Farima

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Journal: IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING Year:2019 | Volume:12 | Issue:4 Page(s): 341-355

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Title

Prostate Cancer Grading and Classification by Combining Deep Features and Stochastic Tissue Features of Pathological Prostate Images

Author(s)

Sezavar Amir | FARSI HASSAN | Farsi Farima | Issue Writer Certificate

Keywords

prostate cancerQ1

gradingQ2

pathological imagesQ1

stochastic tissue featuresQ1

convolutional neural network (CNN)Q1

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.

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APA: Copy

Sezavar, Amir, FARSI, HASSAN, & Farsi, Farima. (2019). Prostate Cancer Grading and Classification by Combining Deep Features and Stochastic Tissue Features of Pathological Prostate Images. IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING, 12(4 ), 341-355. SID. https://sid.ir/paper/81599/en

Vancouver: Copy

Sezavar Amir, FARSI HASSAN, Farsi Farima. Prostate Cancer Grading and Classification by Combining Deep Features and Stochastic Tissue Features of Pathological Prostate Images. IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING[Internet]. 2019;12(4 ):341-355. Available from: https://sid.ir/paper/81599/en

IEEE: Copy

Amir Sezavar, HASSAN FARSI, and Farima Farsi, “Prostate Cancer Grading and Classification by Combining Deep Features and Stochastic Tissue Features of Pathological Prostate Images,” IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING, vol. 12, no. 4 , pp. 341–355, 2019, [Online]. Available: https://sid.ir/paper/81599/en

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