COMPUTER-AIDED DETECTION OF BRAIN TUMORS USING IMAGE PROCESSING TECHNIQUES

Hilal Güven; Ahmet Saygılı

doi:10.17780/ksujes.1447899

EN TR

COMPUTER-AIDED DETECTION OF BRAIN TUMORS USING IMAGE PROCESSING TECHNIQUES

Abstract

Brain tumors are masses formed by the uncontrolled proliferation of cells in the brain. Brain tumors can be malignant or benign and can be fatal if not accurately identified at an early stage. Computer vision processing is used for early diagnosis, monitoring treatment response, and tumor classification. This study aims to detect brain tumors, a significant disease of our time, using image processing techniques. Preprocessing and data augmentation techniques were applied to a dataset of 253 images. Initially, CNNs were used for tumor detection, but transfer learning was employed for better results. Pre-trained VGG-16, DenseNet-121, ResNet-50, and MobileNet_V2 architectures were used. The model, adapted with transfer learning, achieved better performance with less data by adding a customized output layer for brain tumor detection. Experiments showed the best results with VGG-16, achieving 84.61% accuracy before data augmentation and 92.31% after augmentation. Compared to other studies, the post-augmentation accuracy rate was observed to be better than many others. The study also compares results from other deep learning architectures. Summarizing the current technological advancements in various tumor categories may help researchers understand future trends.

Keywords

GÖRÜNTÜ İŞLEME TEKNIKLERI KULLANARAK BEYIN TÜMÖRLERININ BILGISAYAR DESTEKLI TESPITI

Öz

Beyin tümörleri, beyindeki hücrelerin kontrolsüz çoğalmasıyla oluşan kitlelerdir. Beyin tümörleri malign veya benign olabilirler ve erken aşamada doğru bir şekilde tanımlanmazsa ölümcül olabilirler. Bilgisayarlı görü işleme, erken teşhis, tedavi yanıtının izlenmesi ve tümör sınıflandırması için kullanılır. Çalışmada görüntü işleme teknikleri kullanılarak günümüzün önemli bir hastalık olan beyin tümörlerini tespit etmek amaçlanmaktadır. Bu doğrultuda, 253 görüntüden oluşan veri kümesi üzerinde önişleme teknikleri ve veri artırma teknikleri uygulanmıştır. Beyin tümörlerinin tespiti için öncelikle CNN kullanılmıştır ancak daha iyi sonuçlar elde etmek için transfer öğrenme yöntemi kullanılmıştır. Beyin tümörlerinin tespiti için önceden eğitilmiş olan VGG-16, DenseNet-121, ResNet-50, MobileNet_V2 mimarileri kullanılmıştır. Transfer öğrenme ile model, beyin tümörü tespiti için özelleştirilmiş bir çıkış katmanı ekleyerek daha az veri ile daha iyi performans elde edilmiştir. Deneyler sonucunda en iyi oranları VGG-16 mimarisi ile veri artırma öncesi %84.61, veri artırma sonrasında %92.31 doğruluk oranı elde edilmiştir. Diğer çalışmalarla karşılaştırıldığında, veri artırma sonrası elde edilen doğruluk oranının birçok çalışmadan daha iyi olduğu gözlemlenmiştir. Çalışmada ayrıca diğer derin öğrenme mimarilerinden elde edilen sonuçlar karşılaştırılarak çalışmada ayrıca sunulmuştur. Ayrıca, çeşitli tümör kategorilerindeki mevcut teknolojik ilerlemelerin özetlenmesi, araştırmacıların gelecekteki eğilimleri anlamalarına yardımcı olabilir.

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Görüntü İşleme , Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Hilal Güven
0000-0002-7461-4510
Türkiye

Ahmet Saygılı ^*
0000-0001-8625-4842
Türkiye

Yayımlanma Tarihi

3 Eylül 2024

Gönderilme Tarihi

6 Mart 2024

Kabul Tarihi

14 Haziran 2024

Yayımlandığı Sayı

Yıl 1970 Cilt: 27 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1447899

IZ

https://izlik.org/JA45FX84DG

Kaynak Göster

RIS / Bibtex

APA

Güven, H., & Saygılı, A. (2024). COMPUTER-AIDED DETECTION OF BRAIN TUMORS USING IMAGE PROCESSING TECHNIQUES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 999-1018. https://doi.org/10.17780/ksujes.1447899

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