An Artificial Intelligence Based Hybrid Model Proposal for the Detection of Breast Cancer

Öz

Breast cancer is a type of cancer that usually occurs in the lobule, duct and connective tissue regions of the breast and is caused by the abnormal movement of cells in these regions. It is among the most common types of cancer among women in general. When the disease is detected early, cancerous cells can affect other organs through the blood and lymphatic vessels (metastasis state). Therefore, early diagnosis and treatment of breast cancer is important. In this study, an artificial intelligence-based early diagnosis system that can classify between benign and malignant types of breast cancer is proposed. Residual blocky neural network models are used in the proposed approach. Type-based features were extracted by adding a new fully connected layer to the last layer of ResNet models. In the next step, a new feature set was created by combining the features obtained from the fully connected layers. Softmax and machine learning methods (support vector machines, nearest neighbor method, etc.) were used in the classification process. With the proposed approach, 100% overall accuracy was obtained from all the methods used in the classification process. In this study, it was observed that obtaining and combining type-based fully connected layers positively affected the performance of experimental analyzes.

Anahtar Kelimeler

• Artificial intelligence
• feature selection
• machine learning
• breast cancer
• classification

Meme Kanserinin Tespiti için Yapay Zekâ Tabanlı Hibrit Bir Model Önerisi

Öz

Meme kanseri genellikle memenin lobül, kanal ve bağ dokusu bölgelerinde meydana gelen ve bu bölgelerdeki hücrelerin anormal bir şekilde hareketinden meydana gelen kanser türüdür. Genel olarak bayanlar arasında en sık görülen kanser türleri arasında yer almaktadır. Hastalık erkenden fark edilmeği zaman kan ve lenf damarları yoluyla diğer organlara kanserli hücreler etki edebilir (metastaz durumu). Dolayısıyla meme kanserinin erken tanı ve tedavisi önemlidir. Bu çalışmada meme kanserinin iyi huylu ve kötü huylu türleri arasında sınıflandırma yapabilen yapay zekâ tabanlı erken tanı sistemi önerilmiştir. Önerilen yaklaşımda artık bloklu evrişimsel sinir ağı modelleri kullanıldı. ResNet modellerinin son katmanına yeni bir tam bağlantılı katman eklenerek tür tabanlı öznitelikler çıkartıldı. Bir sonraki aşamada tam bağlantılı katmanlardan elde edilmiş öznitelikler birleştirilerek yeni bir özellik seti oluşturuldu. Sınıflandırma sürecinde softmax ve makine öğrenme yöntemleri (destek vektör makineleri, en yakın komşu yöntemi, vb.) kullanıldı. Önerilen yaklaşım ile sınıflandırma sürecinde kullanılan tüm yöntemlerden %100 genel doğruluk başarısı elde edildi. Bu çalışmada tür tabanlı tam bağlantılı katmanların elde edilmesi ve birleştirilmesi deneysel analizlerin performansını olumlu etkilediği gözlenmiştir.

Anahtar Kelimeler

• Yapay zekâ
• özellik seçimi
• makine öğrenmesi
• meme kanseri
• sınıflandırma.

Kaynakça

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