Detection of Stomach Dysplasia by MLP Classifier using Conventional Neural Networks

Seda Sağıroğlu; Seda Arslan Tuncer; Birgül Karahan; İbrahim Hanifi Özercan

Makale detayları

Detection of Stomach Dysplasia by MLP Classifier using Conventional Neural Networks

Yazarlar:

Seda Sağıroğlu | Seda Arslan Tuncer | Birgül Karahan | İbrahim Hanifi Özercan

Yayıncı:

Fırat University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 1, pp.291-300

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001302437

pISSN:

1308-9072

Anahtar kelimeler:

Özet:

 As seen in research conducted in the world and in Turkey in recent years, stomach cancer ranks first compared to other types of cancer. It is known as a type of cancer that is very difficult to detect. Gastric dysplasia is when the cells lining the inner surface of the stomach stop growing and developing normally. These cells begin to grow and multiply uncontrollably, then turn into stomach cancer. The main goal is to minimize this disease before it turns into cancer. Gastric dysplasia is divided into two types: low-grade and high-grade dysplasia. High-grade dysplasia is a precursor to cancer. Therefore, early diagnosis is extremely important for the diagnosis and treatment of gastric dysplasia. This study aims to detect dysplasia at an early stage by using deep learning and convolutional neural networks techniques for the detection of gastric dysplasia. In the study, feature extraction was performed on histopathological images using multiple Convolutional Neural Networks ESA models. The data, divided into two label classes: patient and healthy, were compared with the help of deep learning architectures and classifiers.

 As seen in research conducted in the world and in Turkey in recent years, stomach cancer ranks first compared to other types of cancer. It is known as a type of cancer that is very difficult to detect. Gastric dysplasia is when the cells lining the inner surface of the stomach stop growing and developing normally. These cells begin to grow and multiply uncontrollably, then turn into stomach cancer. The main goal is to minimize this disease before it turns into cancer. Gastric dysplasia is divided into two types: low-grade and high-grade dysplasia. High-grade dysplasia is a precursor to cancer. Therefore, early diagnosis is extremely important for the diagnosis and treatment of gastric dysplasia. This study aims to detect dysplasia at an early stage by using deep learning and convolutional neural networks techniques for the detection of gastric dysplasia. In the study, feature extraction was performed on histopathological images using multiple Convolutional Neural Networks ESA models. The data, divided into two label classes: patient and healthy, were compared with the help of deep learning architectures and classifiers.

 As seen in research conducted in the world and in Turkey in recent years, stomach cancer ranks first compared to other types of cancer. It is known as a type of cancer that is very difficult to detect. Gastric dysplasia is when the cells lining the inner surface of the stomach stop growing and developing normally. These cells begin to grow and multiply uncontrollably, then turn into stomach cancer. The main goal is to minimize this disease before it turns into cancer. Gastric dysplasia is divided into two types: low-grade and high-grade dysplasia. High-grade dysplasia is a precursor to cancer. Therefore, early diagnosis is extremely important for the diagnosis and treatment of gastric dysplasia. This study aims to detect dysplasia at an early stage by using deep learning and convolutional neural networks techniques for the detection of gastric dysplasia. In the study, feature extraction was performed on histopathological images using multiple Convolutional Neural Networks ESA models. The data, divided into two label classes: patient and healthy, were compared with the help of deep learning architectures and classifiers.

 Son yıllarda Dünyada ve Türkiyede yapılan araştırmalarda da görüldüğü üzere mide kanseri diğer kanser türlerine oranla ilk sıralarda yer almaktadır. Tespit edilme açısından da oldukça zorluk çekilen bir kanser türü olarak bilinmektedir. Mide displazisi, midenin iç yüzeyini kaplayan hücrelerin normal şekilde büyümesini ve gelişmesini durdurmasıdır. Bu hücreler, kontrolsüz bir şekilde büyümeye ve çoğalmaya başlarlar, sonrasında mide kanserine dönüşmektedirler. Bu hastalığı kansere dönüşmeden en aza indirgeyebilmek temel hedeftir. Mide displazisi düşük dereceli ve yüksek dereceli displazi olmak üzere iki şekilde ayrılır. Yüksek dereceli displazi, kanserin bir öncesidir. Bu yüzden mide displazinin tanı ve tedavisi için erken dönemde teşhis son derece önem taşımaktadır. Bu çalışmada, mide displazinin tespiti için derin öğrenme ve evrişimsel sinir ağları teknikleri kullanılarak displazi durumunun erken evrede tespiti amaçlanmaktadır. Çalışmada histopatolojik görüntüler üzerinde birden fazla Evrişimsel Sinir Ağları ESA modeli kullanılarak özellik çıkarma işlemi yapılmıştır. Hasta ve sağlıklı olmak üzere iki etiket sınıfına ayrılan veri derin öğrenme mimarileri ve sınıflandırıcılar yardımıyla karşılaştırılmıştır.

Makale detayları

Detection of Stomach Dysplasia by MLP Classifier using Conventional Neural Networks

Yazarlar:

Seda Sağıroğlu | Seda Arslan Tuncer | Birgül Karahan | İbrahim Hanifi Özercan

Yayıncı:

Fırat University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 1, pp.291-300

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001302437

pISSN:

1308-9072

Deep Learning

Gastric Dysplasia Classification

Convolutional Neural Networks (CNN)

Multi-layer Perceptron (MLP).Derin Öğrenme

Mide Displazi

Sınıflandırma

ESA

Çok Katmanlı Algılayıcı (ÇKA)

Sayı

Makale detayları

Yazarlar: Seda Sağıroğlu | Seda Arslan Tuncer | Birgül Karahan | İbrahim Hanifi Özercan

Yayıncı: Fırat University

Ana Başlıklar: Derin Öğrenme

Dergi: Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 1, pp.291-300

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001302437

pISSN: 1308-9072

Sayı

Yazarlar:

Seda Sağıroğlu | Seda Arslan Tuncer | Birgül Karahan | İbrahim Hanifi Özercan

Yayıncı:

Fırat University

Ana Başlıklar:

Derin Öğrenme

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 1, pp.291-300

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001302437

pISSN:

1308-9072