Skin Cancer Recognition Using Compact Deep Convolutional Neural Network

Year 2023, , 787 - 797, 18.10.2023

Alhaji Balla Fofanah , Emre Özbilge , Yonal Kırsal

https://doi.org/10.21605/cukurovaumfd.1377752

Abstract

Skin cancer is a common form of cancer that affects millions of people worldwide. Early detection and accurate diagnosis of skin cancer are crucial for effective treatment and management of the disease. There has been a growing interest in using deep learning techniques and computer vision algorithms to develop automated skin cancer detection systems in recent years. Among these techniques, convolutional neural networks (CNN) have shown remarkable performance in detecting and classifying skin lesions. This paper presents a comprehensive study using CNN and deep learning techniques for skin cancer detection using the International Skin Imaging Collaboration (ISIC) dataset. The proposed architecture is a compact deep CNN that is trained using a dataset of benign and malignant skin lesion images. The proposed architecture has achieved 84.8% accuracy, 83.8% TPR, 83.7% TNR, 81.6% F1-score and 80.5% precision for performance evaluation. The experimental results show promising results for the accurate and efficient detection of skin cancer, which has the potential to improve the diagnosis and treatment of this life-threatening disease.

Keywords

Skin cancer, Convolutional neural network, Deep learning, Isic dataset, TensorFlow

Özel Derin Konvolüsyonel Sinir Ağı Kullanarak Cilt Kanseri Tanıma

Abstract

Cilt kanseri, dünya genelinde milyonlarca insanı etkileyen ciddi ve yaygın bir kanser türüdür. Cilt kanserinin erken teşhisi ve doğru tanısı, hastalığın etkili bir şekilde tedavi edilmesi ve yönetilmesi için önemlidir. Son yıllarda derin öğrenme tekniklerinin ve bilgisayarlı görü algoritmalarının otomatik cilt kanseri tespit sistemleri geliştirmek için kullanılması konusunda büyük bir ilgi bulunmaktadır. Bu teknikler arasında konvolüsyonel sinir ağları (CNN), cilt lezyonlarını tespit etme ve sınıflandırmada dikkate değer bir performans göstermiştir. Bu makalede, Uluslararası Cilt Görüntüleme İşbirliği (ISIC) veri seti kullanılarak cilt kanseri tespiti için CNN ve derin öğrenme tekniklerinin kapsamlı bir çalışmasını sunmaktayız. Önerilen mimari, özelleştirilmiş derin CNN kullanılarak eğitilmiş olan, benign ve malign cilt lezyonu görüntülerinin bir veri setini kullanmaktadır. Önerilen mimari, performans değerlendirmesi için 84.8% doğruluk, 83.8% TPR, 83.7% TNR, 81.6% F1-skoru ve 80.5% hassaslık elde etmiştir. Deneysel sonuçlar, cilt kanserinin doğru ve verimli bir şekilde tespiti için umut verici sonuçlar göstermektedir ve bu yaşamı tehdit eden hastalığın teşhis ve tedavisini iyileştirme potansiyeline sahiptir.

Keywords

Cilt kanseri, Konvolüsyonel sinir ağı, Derin öğrenme, Isic veri sesti, Tensör Akışı

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