Görsel dönüştürücüler kullanılarak göğüs BT görüntülerinden akciğer kanseri alt tipi sınıflandırması

Öz

Akciğer kanseri, dünya genelinde önemli bir sağlık sorunu olmaya devam etmekte ve kanser kaynaklı ölümlerin başlıca nedenlerinden biri olarak öne çıkmaktadır; bu durum hasta sağkalım oranlarını artırmak için doğru ve erken tanı yaklaşımlarının geliştirilmesi gerekliliğini açıkça ortaya koymaktadır. Bu çalışmada göğüs BT (bilgisayarlı tomografi) görüntülerinden akciğer kanseri alt tiplerinin otomatik sınıflandırılması amacıyla dönüştürücü (transformer) tabanlı bir derin öğrenme modeli önerilmektedir. Model, dört tanısal kategori (adenokarsinom, skuamöz hücreli karsinom, büyük hücreli karsinom ve normal dokular) içeren halka açık Chest CT-Scan Images Dataset veriseti üzerinde eğitilip değerlendirilmiştir. Etiket normalizasyonu, görüntü yeniden boyutlandırma, veri dengelenmesi ve RandAugment yöntemiyle veri artırımı adımlarını içeren kapsamlı bir ön işleme hattı uygulanmıştır. Tıbbi görüntülerde uzun menzilli uzamsal bağımlılıkları yakalamadaki üstün kapasitesi nedeniyle Görsel Dönüştürücü (ViT-Large) mimarisi tercih edilmiştir. Deneysel sonuçlar modelin test kümesi üzerinde %98,5 doğruluk ve 0,980 makro ortalamalı F1-skoru elde ettiğini göstermektedir. Bu çalışmanın yeniliği klinik olarak anlamlı bir veri kümesinde ViT-Large omurgasıyla entegre edilen güçlü bir ön işleme stratejisinin ve jeton düzeyinde eğitim kararlılığını artırıcı yöntemlerin bir araya getirilmesiyle güncel en iyi (state-of-the-art) performansın elde edilmesinde yatmaktadır.

Anahtar Kelimeler

• akciğer kanseri sınıflandırması
• derin öğrenme
• göğüs BT görüntüleri
• görsel dönüştürücüler
• tıbbi görüntü analizi

Lung cancer subtype classification from chest CT scans using vision transformers

Öz

Lung cancer is a major global health concern and remains a leading cause of cancer-related mortality, highlighting the urgent need for accurate and early diagnostic approaches to improve patient survival rates. In this study, we propose a transformer-based deep learning model for the automatic classification of lung cancer subtypes from chest CT images. The model is trained and evaluated on the publicly available Chest CT-Scan Images Dataset, which includes four diagnostic categories: adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and normal tissues. A comprehensive preprocessing pipeline was employed, including label normalization, image resizing, data balancing, and augmentation using RandAugment. We adopted the Vision Transformer (ViT-Large) architecture due to its superior capacity for capturing long-range spatial dependencies in medical imaging. Experimental results demonstrate significant performance gains, with the model achieving 98.5% accuracy and a macro-averaged F1-score of 0.980 on the test set. The novelty of this study lies in the integration of a robust preprocessing strategy with a ViT-Large backbone and token-level training stability enhancements to achieve state-of-the-art performance on a clinically relevant dataset.

Anahtar Kelimeler

• lung cancer classification
• deep learning
• chest CT scans
• vision transformers
• medical image analysis

Kaynakça

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