Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm

This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images’ features were extracted based on the convolutional neural network. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm to select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method’s performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others.

Erişime Açık
Görüntülenme
110
13.07.2023 tarihinden bu yana
İndirme
1
13.07.2023 tarihinden bu yana
Son Erişim Tarihi
06 Eylül 2024 15:34
Google Kontrol
Tıklayınız
Tam Metin
Tam Metin İndirmek için tıklayın Ön izleme
Detaylı Görünüm
Eser Adı
(dc.title)
Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm
Yazar
(dc.contributor.author)
Cevat Rahebi
Yayın Yılı
(dc.date.issued)
2023
Tür
(dc.type)
Makale
Özet
(dc.description.abstract)
This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images’ features were extracted based on the convolutional neural network. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm to select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method’s performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others.
Açık Erişim Tarihi
(dc.date.available)
2023-05-17
Yayıncı
(dc.publisher)
MDPI
Dil
(dc.language.iso)
En
Konu Başlıkları
(dc.subject)
colon disease diagnose
Konu Başlıkları
(dc.subject)
convolutional neural network
Konu Başlıkları
(dc.subject)
grasshopper optimization algorithm
Konu Başlıkları
(dc.subject)
machine learning
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.14081/1899
Dergi
(dc.relation.journal)
DIAGNOSTICS
Dergi Sayısı
(dc.identifier.issue)
10
Esere Katkı Sağlayan
(dc.contributor.other)
Rahebi, Javad
Esere Katkı Sağlayan
(dc.contributor.other)
Mohamed, Amna Ali
Esere Katkı Sağlayan
(dc.contributor.other)
Ray, Mayukh K.
Esere Katkı Sağlayan
(dc.contributor.other)
Roy, Sudipta
Esere Katkı Sağlayan
(dc.contributor.other)
Hancerliogullari, Aybaba
DOI
(dc.identifier.doi)
10.3390/diagnostics13101728
Orcid
(dc.identifier.orcid)
0000-0001-9875-4860
Bitiş Sayfası
(dc.identifier.endpage)
14
Başlangıç Sayfası
(dc.identifier.startpage)
1
Dergi Cilt
(dc.identifier.volume)
13
wosquality
(dc.identifier.wosquality)
Q2
wosauthorid
(dc.contributor.wosauthorid)
DNF-7937-2022
Department
(dc.contributor.department)
Yazılım Mühendisliği
Wos No
(dc.identifier.wos)
WOS:000998200500001
Veritabanları
(dc.source.platform)
Wos
Veritabanları
(dc.source.platform)
Scopus
Veritabanları
(dc.source.platform)
PubMed
Analizler
Yayın Görüntülenme
Yayın Görüntülenme
Erişilen ülkeler
Erişilen şehirler
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.
Tamam

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms