Dermoskopik görüntülerde lezyon bölütleme için iyileştirme önerileri

Öz

Görüntülerde bölütleme görevini gerçekleştiren U-Net mimarisi tıp alanında da oldukça başarılı sonuçlar elde etmiştir. Fakat daha iyi sonuçlar için mimarinin iyileştirilmesine de gerek vardır. Bu makalede, U-Net modelinin kodlayıcı bölümü için bazı iyileştirme önerileri sunulmaktadır ve uygulanan mimarinin dermoskopik görüntülerde lezyon bölütleme işlemi için bölütleme başarısı değerlendirilmektedir. Araştırma, PH2 ve “Uluslararası Cilt Görüntüleme İşbirliği” (ISIC-2016 ve ISIC-2017) veri kümeleri ile yapılmıştır. Geleneksel veri artırma yöntemi, seçilen PH2 veri kümesi örneklerine uygulanmıştır. Önerilen model (EnecaU-Net) ve U-Net modelinin PH2 veri kümesi ile elde edilen sonuçları karşılaştırılmıştır. Ayrıca bu makalede dermoskopik görüntülerde lezyon bölütleme işlemi için modelin bölütleme başarısı üzerinde etki gösteren karıştırma veri artırma yöntemi de incelenmiştir. Bu inceleme, ISIC-2016 veri kümesi ile yapılmıştır ve veri artırma işlemi uygulanmayan aynı miktardaki ISIC-2017 veri kümesi ile yapılan inceleme sonuçları karşılaştırılmıştır. Değerlendirme aşamasında model başarısının ölçümü için öncelikle Dice ve Jaccard (IoU) ölçütlerini kullanmış olmakla beraber özgüllük, duyarlılık ve doğruluk ölçütlerinden de faydalanılmıştır. Elde ettiğimiz sonuçlara göre, dermoskopik görüntülerde lezyon bölütleme işlemi için uygulanan EnecaU-Net modelinin bölütleme başarısı yüksektir ve uygulanan karıştırma veri artırma yöntemi, EnecaU-Net modelinin bölütleme başarısını iyileştirmektedir. Önerilen modelin ulaştığı ortalama test sonuçları, Dice ve Jaccard değerleri açısından sırasıyla ISIC-2016 için %88,05 ve %80,30 ve ISIC-2017 için %83,09 ve %74,54’tür.

Anahtar Kelimeler

• Evrişimsel sinir ağı
• karıştırma veri artırma
• bölütleme
• dermoskopik lezyon
• U-Net

Improvement proposals for lesion segmentation on dermoscopic images

Öz

The U-Net architecture, which performs segmentation on images, has also achieved very successful results in the medical field. However, there is also a need to improve the U-Net architecture for better results. In this article, some improvement proposals are presented for the U-Net model's encoder part, and the segmentation success of the implemented architecture for segmentation of dermoscopic image lesions is evaluated. The PH2 dataset and the "International Skin Imaging Collaboration" datasets (ISIC-2016 and ISIC-2017) were used for the research. The traditional data augmentation method was applied to the selected PH2 dataset samples. The results of the proposed model (EnecaU-Net) and the U-Net model obtained with the PH2 dataset were compared. Furthermore, in this article, the mix data augmentation method, which has an influence on the model's segmentation success, is examined for lesion segmentation on dermoscopic images. This investigation was made with the ISIC-2016 dataset, and its experimental results were compared with the same amount of the ISIC-2017 dataset that didn't apply data augmentation operations. Although, during the evaluation phase, Dice and Jaccard (IoU) metrics were used primarily to measure the success of the model, specificity, sensitivity, and accuracy criteria were also used. According to our results, the segmentation success of the EnecaU-Net model applied for lesion segmentation on dermoscopic images is high, and the applied mix data augmentation method improves the segmentation success of the EnecaU-Net model. The average test results achieved by the proposed model are 88.05% and 80.30% for ISIC-2016 and 83.09% and 74.54% for ISIC-2017 in terms of the Dice and Jaccard values, respectively.

Anahtar Kelimeler

• Convolutional neural network
• mix data augmentation
• segmentation
• dermoscopic lesion
• U-Net

Kaynakça

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