Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Diagnosis and Classification of The Degree of Malignancy of Prostate Cancer in Pathological Images Using Ensemble Deep Learning

Document Type : Persian Original Article

Authors
Hamed Fazlollahi
Nasser Mehrshad
Sajad Mohamadzadeh
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
Abstract
Prostate cancer is considered as one of the most common diseases of men worldwide after lung cancer in this century. Diagnosis and classification of medical images is a very challenging task. Timely diagnosis and prevention of this disease helps significantly in the treatment process and preventing its progression to other tissues. In order to determine the degree of the disease, the tissues are sampled and the type of degree is determined by examining the pathology structure. The most common method used to diagnose prostate cancer is microscopic observation of stained biopsies by a pathologist and Gleason grading of pathology image. In the most recent classification, the prostate tissue is divided into five grades, where grade one is the most benign condition and grade five indicates the worst condition of the disease. In this article, a new method has been introduced that combines the output of the classifications of three models of deep learning. The Transfer Learning method trained on a set of images (ImageNet) has been used. To evaluate the trained networks, test samples are applied to the networks. By using two rules of the combination of classifiers, such as majority voting and averaging, the test image will be assigned to a class. In order to evaluate the proposed method, this method has been applied to the database of the International Society of Urological Pathology (ISUP). The proposed method in this research is based on ensemble deep learning, which provides detection accuracy about 97.2% and 97.33% in prostate cancer according, respectively, for two rules of combining voting and averaging. Compared to other conventional methods for grading prostate cancer pathology images, it has increased the accuracy of more than 4% in diagnosis and classification.
Keywords
Ensemble deep learning
combination of classifiers
prostate cancer
pathology images
Transfer Learning
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Journal of Soft Computing and Information Technology
Volume 13, Issue 2
Summer - September
Summer 2024
Pages 13-24