VGGNET VE CBAM DİKKAT MEKANİZMASININ ENDOSKOPİK MESANE DOKU GÖRÜNTÜLERİNE UYGULANMASI

Yıl 2024, , 38 - 47, 18.03.2024

Birkan Büyükarıkan

https://doi.org/10.46236/umbd.1389687

Öz

Tıbbi hastalıkların tespiti, tanısı ve izlenmesi amacıyla gerçekleştirilecek görevlerde, ilgili bilgilerin öne çıkartılması ve ilgisiz bilgilerin bastırılmasında Evrişimsel sinir ağları (ESA) yaygın olarak kullanılmaktadır. Ancak ESA modellerinin hesaplama karmaşıklığı, özellik kalitesi sorunu ve artan özellik boyutu gibi nedenler hastalığın tespit performansını zorlamaktadır. Son zamanlarda, ESA modellerinin performansını artırmak için dikkat mekanizmaları kullanılmaktadır, bu da sorunların üstesinden gelmeye yardımcı olmaktadır. Evrişimsel Blok Dikkat Modülü (CBAM) dikkat mekanizması, içerisinde barındırdığı modüller yardımıyla ilgili karmaşık özellikleri çıkarmak için geliştirilmiş bir yöntemdir. ESA modeliyle bu mekanizmanın birleştirilmesi, modelin performansını önemli ölçüde iyileştirilir. Çalışmanın amacı VGGNet ve CBAM dikkat mekanizmasını birleştirerek oluşturulan modellerle mesane tümörünün sınıflandırılmasıdır. Çalışmada VGGNet ve VGGNet+CBAM modellerinin performanslarını karşılaştırmak için doğruluk, kesinlik, duyarlılık, F1-skor ve eğri altında kalan alan (AUC) metrikleri kullanılarak deneyler gerçekleştirilmiştir. Sonuçlara göre VGG19+CBAM modeli, doğruluk, kesinlik, duyarlılık, F1-skor ve AUC ölçütleri açısından en yüksek performans değerlerini göstermiştir. Bu modelin doğruluk, kesinlik, duyarlılık, F1-skor ve AUC ölçütlerinin değerleri sırasıyla 0,990, 0,992, 0,984, 0,986 ve 0,994’tür. VGGNet+CBAM modelleri, VGGNet modellerinden daha iyi performans göstermiştir. Elde edilen performans değerlerine dayanarak, önerilen yaklaşımın mesane tümörü teşhisinde etkili olduğu görülmektedir.

Anahtar Kelimeler

Evrişimsel sinir ağı, CBAM dikkat mekanizması, endoskopik mesane dokusu

APPLICATION OF VGGNET AND CBAM ATTENTION MECHANISM TO ENDOSCOPIC BLADDER TISSUE IMAGES

Öz

Convolutional neural networks (CNNs) are widely used to highlight relevant information and suppress irrelevant information in tasks to be performed for detecting, diagnosing, and monitoring medical diseases. However, reasons such as computational complexity, feature quality problems, and increasing feature size challenge the disease detection performance of CNN models. In recent times, attention mechanisms have been used to enhance the performance of CNN models, helping to overcome challenges. The Convolutional Block Attention Module (CBAM) attention mechanism is a method developed to extract relevant complex features with the help of the modules it contains. The integration of this mechanism with the CNN model significantly improves the performance of the model.
This study aims to classify bladder tissue with models created by combining VGGNet and CBAM attention mechanism. Experiments were carried out using accuracy, precision, recall, F1-score, and area under the curve metrics (AUC) to compare the performances of VGGNet and VGGNet+CBAM models. According to the results, the VGG19+CBAM model has demonstrated the highest performance values in terms of accuracy, precision, recall, F1-score, and AUC criteria. The accuracy, precision, recall, F1-score, and AUC values of this model are 0.990, 0.992, 0.984, 0.986, and 0.994, respectively. VGGNet+CBAM models have shown better performance than VGGNet models. Based on the achieved performance values, it is evident that the proposed approach is effective in the diagnosis of bladder tumors.

Anahtar Kelimeler

Convolutional neural network, CBAM attention mechanism, endoscopic bladder tissue

Kaynakça

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