Oral Cancer Classification with CNN Based State-of-the-art Transfer Learning Methods

Kaan Gümele; Muhammet Sinan Başarslan

Makale detayları

Oral Cancer Classification with CNN Based State-of-the-art Transfer Learning Methods

Yazarlar:

Kaan Gümele | Muhammet Sinan Başarslan

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Görüntüleme

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 1, pp.94-101

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001331046

pISSN:

2619-8991

Anahtar kelimeler:

Özet:

 The importance of oral and dental health closely affects other vital organs. In this study, CNN-based transfer learning models are built on histopathologic and intraoral images with benign and malignant lesions. Histopathologic and intraoral images from two different sources have benign or malignant classes of lesions in the mouth. EfficientNetB7, ResNet50, VGG16, and VGG19, Xception, ConvNextBase, and MobileNetV2 were used as transfer learning methods. Model training was performed with 80-20 train test separation and 20 validation separation on the train set. Accuracy Acc, Precision Prec, Recall Rec, and F1-score F1 metrics were used to evaluate the model. In histopathologocial images, ResNet50 was ahead with 0.8125 Acc and 0.8525 F1. In intraoral images, ConvNextBase with 0.84 Acc, and 0.80 F1 was found to be more accurate.

 The importance of oral and dental health closely affects other vital organs. In this study, CNN-based transfer learning models are built on histopathologic and intraoral images with benign and malignant lesions. Histopathologic and intraoral images from two different sources have benign or malignant classes of lesions in the mouth. EfficientNetB7, ResNet50, VGG16, and VGG19, Xception, ConvNextBase, and MobileNetV2 were used as transfer learning methods. Model training was performed with 80-20 train test separation and 20 validation separation on the train set. Accuracy Acc, Precision Prec, Recall Rec, and F1-score F1 metrics were used to evaluate the model. In histopathologocial images, ResNet50 was ahead with 0.8125 Acc and 0.8525 F1. In intraoral images, ConvNextBase with 0.84 Acc, and 0.80 F1 was found to be more accurate.

 The importance of oral and dental health closely affects other vital organs. In this study, CNN-based transfer learning models are built on histopathologic and intraoral images with benign and malignant lesions. Histopathologic and intraoral images from two different sources have benign or malignant classes of lesions in the mouth. EfficientNetB7, ResNet50, VGG16, and VGG19, Xception, ConvNextBase, and MobileNetV2 were used as transfer learning methods. Model training was performed with 80-20 train test separation and 20 validation separation on the train set. Accuracy Acc, Precision Prec, Recall Rec, and F1-score F1 metrics were used to evaluate the model. In histopathologocial images, ResNet50 was ahead with 0.8125 Acc and 0.8525 F1. In intraoral images, ConvNextBase with 0.84 Acc, and 0.80 F1 was found to be more accurate.

 The importance of oral and dental health closely affects other vital organs. In this study, CNN-based transfer learning models are built on histopathologic and intraoral images with benign and malignant lesions. Histopathologic and intraoral images from two different sources have benign or malignant classes of lesions in the mouth. EfficientNetB7, ResNet50, VGG16, and VGG19, Xception, ConvNextBase, and MobileNetV2 were used as transfer learning methods. Model training was performed with 80-20 train test separation and 20 validation separation on the train set. Accuracy Acc, Precision Prec, Recall Rec, and F1-score F1 metrics were used to evaluate the model. In histopathologocial images, ResNet50 was ahead with 0.8125 Acc and 0.8525 F1. In intraoral images, ConvNextBase with 0.84 Acc, and 0.80 F1 was found to be more accurate.

Makale detayları

Oral Cancer Classification with CNN Based State-of-the-art Transfer Learning Methods

Yazarlar:

Kaan Gümele | Muhammet Sinan Başarslan

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Görüntüleme

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 1, pp.94-101

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001331046

pISSN:

2619-8991

Oral cancer

Image processing

Convolutional neural network

Transfer learningOral cancer

Image processing

Convolutional neural network

Transfer learning

Sayı

Makale detayları

Yazarlar: Kaan Gümele | Muhammet Sinan Başarslan

Yayıncı: Karyay Karadeniz Yayımcılık

Ana Başlıklar: Biyomedikal Görüntüleme

Dergi: Black Sea Journal of Engineering and Science Vol.8 Issue 1, pp.94-101

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001331046

pISSN: 2619-8991

Sayı

Yazarlar:

Kaan Gümele | Muhammet Sinan Başarslan

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Görüntüleme

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 1, pp.94-101

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001331046

pISSN:

2619-8991