DIAGNOSIS OF PROSTATE CANCER WITH ENHANCED EFFICIENCY USING FINE-TUNED CNN AND TRANSFER LEARNING

Murat Sarıateş; Erdal Özbay

doi:10.17780/ksujes.1459277

TR EN

İNCE-AYAR İLE ETKİNLİĞİ ARTIRILMIŞ ESA VE TRANSFER ÖĞRENME YÖNTEMLERİYLE PROSTAT KANSERİNİN TESPİTİ

Öz

Kanser insanlar için yüksek riskli hastalıkların başındadır. Prostat vakaları, akciğer kanserinden sonra erkeklerde ikinci sırada yer almakta ve erken teşhisi hayati önem taşımaktadır. Prostat kanserinin teşhisinde yapay zeka teknolojilerinden faydalanılmaya başlanmış, daha etkili ve hassas sonuçlar elde edilerek insan odaklı yöntemlerdeki potansiyel hatalarının önüne geçilmiştir. Bu çalışmada prostat kanserinin teşhisinde sınıflandırma performansını arttırabilmek adına makine öğrenmesi yöntemlerinden farklı olarak daha az eğitim verisi ile daha yüksek başarı ve öğrenme kabiliyetine sahip transfer öğrenme yöntemi ve ince-ayar işlemleri uygulanmıştır. Prostat kanseri MR görüntülerinden oluşan ‘significant’ ve ‘not-significant’ olmak üzere iki sınıflı veri setine, özellik çıkarımı yaklaşımıyla Alexnet, Densenet201, Googlenet ve Vgg16 modelleriyle sınıflandırılarak sırasıyla %71,40, %72,05, %65,72 ve %80,13 doğruluk sonuçları elde edilmiştir. Bu oranları arttırabilmek adına ön-eğitimli transfer öğrenme modelleri kullanılmış ve sırasıyla %89,74, %94,32, %85,59 ve %91,05 doğruluk sonuçlarına ulaşılmıştır. Densenet201 modelinde çapraz-doğrulama yöntemi kullanılarak %98,10 doğrulama sonucu elde edilmiştir. DenseNet201 modeli transfer öğrenmede RMSProp optimizasyon yöntemi kombinasyonuyla %98,63 ile en yüksek doğruluk sonucuna ulaşmıştır. Önerilen transfer öğrenme modeli, özellik çıkarımı yöntemine kıyasla yaklaşık %26 oranında bir iyileştirme sağlamıştır.

Anahtar Kelimeler

DIAGNOSIS OF PROSTATE CANCER WITH ENHANCED EFFICIENCY USING FINE-TUNED CNN AND TRANSFER LEARNING

Abstract

Cancer is one of the high-risk diseases for humans. Prostate cases are the second most common disease in men after lung cancer, and early diagnosis is vital. Artificial intelligence technologies have begun to be used in the diagnosis of prostate cancer, and more effective and sensitive results have been obtained, preventing potential errors in human-centered methods. In this study, in order to increase the classification performance in the diagnosis of prostate cancer, transfer learning methods and fine-tuning processes, which have higher success and learning ability with less training data, unlike machine learning methods, were applied. The two-class data set consisting of prostate cancer MR images, ‘significant’ and ‘not-significant’, was classified with Alexnet, Densenet201, Googlenet, and Vgg16 models with the feature extraction approach, and 71.40%, 72.05%, 65%, and 80.13% accuracy results were obtained respectively. To increase these rates, pre-trained transfer learning models were used and accuracy results of 89.74%, 94.32%, 85.59%, and 91.05% were achieved, respectively. A 98.10% validation result was obtained using the cross-validation method in the Densenet201 model. DenseNet201 model achieved the highest accuracy result of 98.63% in transfer learning with the combination of the RMSProp optimization method. The proposed transfer learning model provided an improvement of approximately 26% compared to the feature extraction method.

Keywords

References

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Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Murat Sarıateş
0009-0003-4351-9566
Türkiye

Erdal Özbay ^*
0000-0002-9004-4802
Türkiye

Publication Date

December 3, 2024

Submission Date

March 26, 2024

Acceptance Date

May 6, 2024

Published in Issue

Year 2024 Volume: 27 Number: 4

DOI

https://doi.org/10.17780/ksujes.1459277

IZ

https://izlik.org/JA23DY38CD

Cite

RIS / Bibtex

APA

Sarıateş, M., & Özbay, E. (2024). DIAGNOSIS OF PROSTATE CANCER WITH ENHANCED EFFICIENCY USING FINE-TUNED CNN AND TRANSFER LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1306-1319. https://doi.org/10.17780/ksujes.1459277

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https://doi.org/10.3390/diagnostics15243235