CNN tabanlı derin öğrenme yaklaşımı ile kanser görüntülerinin sınıflandırılması

Year 2023, Volume: 12 Issue: 1, 30 - 38, 15.01.2023

Halit Çetiner

https://doi.org/10.28948/ngumuh.1143693

Abstract

Halk arasında melanoma (mel), dermatofibroma (df), ve vascular (vasc), bening keratosis (bkl), melanocytic nevi (nv), basal cell carcinoma (bcc), actinic keratosis (akiec) olarak bilinen cilt kanserleri yüksek benzerliğe sahiptir. Belirtilen cilt kanserlerinin erken aşamada doğru bir şekilde sınıflandırılması insan yaşamını kurtarması açısından önemlidir. Bu makalede yaygın görülen cilt kanserlerinin sınıflandırma süreçleri için yüksek doğruluklu bir derin öğrenme modeli önerilmiştir. Önerilen model, genel olarak iş yoğunluğu yüksek olan cilt uzmanlarına yardımcı, hızlı tanı ve sınıflandırma yetkinliğine sahip bir modeldir. Birbirine oldukça benzer olan cilt kanserlerinin sınıflandırılmasında, swish ve ReLU aktivasyon fonksiyonlarının avantajlarından faydalanan 30 katmanlı bir CNN modeli önerilmiştir. Bu model kullanılarak akiec, bcc, bkl, df, nv, vasc, mel adlı cilt kanserlerinin sınıflandırılmasında sırasıyla 0.99%, 0.99%, 0.96%, 0.99%, 0.92%, 0.99%, 0.95% F1 score değerleri elde edilmiştir. Akiec, bcc, bkl, df, nv, vasc, mel adlı cilt kanserlerinin sınıflandırılmasında precision ve recall ölçüm metrikleri açısından sırasıyla 0.99%, 0.99, 0.93, 0.99, 0.97, 0.99, 0.94 precision ve 0.99, 0.98, 0.99, 1, 0.87, 1, 0.97 recall değerleri elde edilmiştir. Elde edilen performans sonuçlarına göre önerilen modelin birbirine oldukça benzer yedi farklı cilt kanserini doğru bir şekilde sınıflandırdığı söylenebilir.

Keywords

CNN, Rastgele yeniden örneklendirme algoritması, Cilt kanseri, Derin öğrenme

Classification of cancer images with CNN-based deep learning approach

Abstract

Skin cancers known as melanoma (mel), dermatofibroma (df), and vascular (vasc), benign keratosis (bkl), melanocytic nevi (nv), basal cell carcinoma (bcc), actinic keratosis (akiec) have a high similarity. Accurate classification of specified skin cancers at an early stage is important in terms of saving human life. In this article, a high-accuracy deep learning model is proposed for the classification processes of common skin cancers. The proposed model is a model that helps skin specialists with a high workload and has rapid diagnosis and classification competence. A 30-layer CNN model is proposed that takes advantage of the swish and ReLU activation functions in the classification of highly similar skin cancers. Using this model, 0.99%, 0.99%, 0.96%, 0.99%, 0.92%, 0.99%, 0.95% F1 score values were obtained in the classification of skin cancers named akiec, bcc, bkl, df, nv, vasc, mel, respectively. In terms of precision and recall measurement metrics in the classification of skin cancers named Akiec, bcc, bkl, df, nv, vasc, mel, respectively, 0.99%, 0.99, 0.93, 0.99, 0.97, 0.99, 0.94 precision and 0.99, 0.98, 0.99, 1, 0.87, 1, 0.97 recall values were obtained. Based on the performance results obtained, it can be said that the proposed model correctly classifies seven very similar skin cancers.

Keywords

CNN, Random Over Sampler algorithm, Skin cancer, Deep learning

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