Classification of cancer images with CNN-based deep learning approach

Halit Çetiner

Makale detayları

Classification of cancer images with CNN-based deep learning approach

Yazarlar:

Halit Çetiner

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 1, pp.30-38

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001282502

pISSN:

2564-6605

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

Classification of cancer images with CNN-based deep learning approach

Yazarlar:

Halit Çetiner

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 1, pp.30-38

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001282502

pISSN:

2564-6605

CNN

Random Over Sampler algorithm

Skin cancer

Deep learning

CNN

Rastgele yeniden örneklendirme algoritması

Cilt kanseri

Derin öğrenme

Sayı

Makale detayları

Yazarlar: Halit Çetiner

Yayıncı: Nigde Omer Halisdemir University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 1, pp.30-38

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001282502

pISSN: 2564-6605

Sayı

Yazarlar:

Halit Çetiner

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 1, pp.30-38

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001282502

pISSN:

2564-6605