Classification of Gastrointestinal Diseases in Endoscopic Images: Comparative Analysis of Convolutional Neural Networks and Vision Transformers

Enes Ayan

Article Details

Classification of Gastrointestinal Diseases in Endoscopic Images: Comparative Analysis of Convolutional Neural Networks and Vision Transformers

Authors:

Enes Ayan

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı | Yazılım Mühendisliği (Diğer)

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 3, pp.988-999

Document Type:

Article

Publication Date:

1/2024

Document Language:

English

Document ID (Org No.):

2001325094

pISSN:

2536-4618

Keywords:

Abstract:

 Gastrointestinal GI diseases are a major issue in the human digestive system. Therefore, many studies have explored the automatic classification of GI diseases to reduce the burden on clinicians and improve patient outcomes for both diagnosis and treatment purposes. Convolutional neural networks CNNs and Vision Transformers ViTs in deep learning approaches have become a popular research area for the automatic detection of diseases from medical images. This study evaluated the classification performance of thirteen different CNN models and two different ViT architectures on endoscopic images. The impact of transfer learning parameters on classification performance was also observed. The tests revealed that the classification accuracies of the ViT models were 91.25 and 90.50, respectively. In contrast, the DenseNet201 architecture, with optimized transfer learning parameters, achieved an accuracy of 93.13, recall of 93.17, precision of 93.13, and an F1 score of 93.11, making it the most successful model among all the others. Considering the results, it is evident that a well-optimized CNN model achieved better classification performance than the ViT models.

 Gastrointestinal GI diseases are a major issue in the human digestive system. Therefore, many studies have explored the automatic classification of GI diseases to reduce the burden on clinicians and improve patient outcomes for both diagnosis and treatment purposes. Convolutional neural networks CNNs and Vision Transformers ViTs in deep learning approaches have become a popular research area for the automatic detection of diseases from medical images. This study evaluated the classification performance of thirteen different CNN models and two different ViT architectures on endoscopic images. The impact of transfer learning parameters on classification performance was also observed. The tests revealed that the classification accuracies of the ViT models were 91.25 and 90.50, respectively. In contrast, the DenseNet201 architecture, with optimized transfer learning parameters, achieved an accuracy of 93.13, recall of 93.17, precision of 93.13, and an F1 score of 93.11, making it the most successful model among all the others. Considering the results, it is evident that a well-optimized CNN model achieved better classification performance than the ViT models.

 Gastrointestinal GI diseases are a major issue in the human digestive system. Therefore, many studies have explored the automatic classification of GI diseases to reduce the burden on clinicians and improve patient outcomes for both diagnosis and treatment purposes. Convolutional neural networks CNNs and Vision Transformers ViTs in deep learning approaches have become a popular research area for the automatic detection of diseases from medical images. This study evaluated the classification performance of thirteen different CNN models and two different ViT architectures on endoscopic images. The impact of transfer learning parameters on classification performance was also observed. The tests revealed that the classification accuracies of the ViT models were 91.25 and 90.50, respectively. In contrast, the DenseNet201 architecture, with optimized transfer learning parameters, achieved an accuracy of 93.13, recall of 93.17, precision of 93.13, and an F1 score of 93.11, making it the most successful model among all the others. Considering the results, it is evident that a well-optimized CNN model achieved better classification performance than the ViT models.

 Gastrointestinal GI diseases are a major issue in the human digestive system. Therefore, many studies have explored the automatic classification of GI diseases to reduce the burden on clinicians and improve patient outcomes for both diagnosis and treatment purposes. Convolutional neural networks CNNs and Vision Transformers ViTs in deep learning approaches have become a popular research area for the automatic detection of diseases from medical images. This study evaluated the classification performance of thirteen different CNN models and two different ViT architectures on endoscopic images. The impact of transfer learning parameters on classification performance was also observed. The tests revealed that the classification accuracies of the ViT models were 91.25 and 90.50, respectively. In contrast, the DenseNet201 architecture, with optimized transfer learning parameters, achieved an accuracy of 93.13, recall of 93.17, precision of 93.13, and an F1 score of 93.11, making it the most successful model among all the others. Considering the results, it is evident that a well-optimized CNN model achieved better classification performance than the ViT models.

Article Details

Classification of Gastrointestinal Diseases in Endoscopic Images: Comparative Analysis of Convolutional Neural Networks and Vision Transformers

Authors:

Enes Ayan

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı | Yazılım Mühendisliği (Diğer)

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 3, pp.988-999

Document Type:

Article

Publication Date:

1/2024

Document Language:

English

Document ID (Org No.):

2001325094

pISSN:

2536-4618

Medical Image Classification

Convolutional Neural Networks

Vision Transformers

Fine Tuning

Transfer Learning

Gastrointestinal Diseases

Issues

Article Details

Classification of Gastrointestinal Diseases in Endoscopic Images: Comparative Analysis of Convolutional Neural Networks and Vision Transformers

Authors: Enes Ayan

Publisher: Igdir University

Main Topics: Bilgisayar Yazılımı | Yazılım Mühendisliği (Diğer)

Journal: Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 3, pp.988-999

Document Type: Article

Publication Date: 1/2024

Document Language: English

Document ID (Org No.): 2001325094

pISSN: 2536-4618

Medical Image Classification

Convolutional Neural Networks

Vision Transformers

Fine Tuning

Transfer Learning

Gastrointestinal Diseases

Issues

Authors:

Enes Ayan

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı | Yazılım Mühendisliği (Diğer)

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 3, pp.988-999

Document Type:

Article

Publication Date:

1/2024

Document Language:

English

Document ID (Org No.):

2001325094

pISSN:

2536-4618