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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 13 Sayı: 1

A new Convolutional Neural Network proposal for classifying various images

Çeşitli görüntülerin sınıflandırılması için yeni bir Evrişimsel Sinir Ağı önerisi

Yeşim Tiraki(DUMLUPINAR ÜNİVERSİTESİ)0000-0001-5330-8656
Hasan Temurtaş(DUMLUPINAR ÜNİVERSİTESİ)0000-0001-6738-3024
Soydan Serttaş(DUMLUPINAR ÜNİVERSİTESİ)0000-0001-8887-8675
Çiğdem Bakır(YILDIZ TEKNİK ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ, BİLGİSAYAR MÜHENDİSLİĞİ ANABİLİM DALI)0000-0001-8482-2412
Pages
262–278
Publication Date
2024-01-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

With the proposed CNN model, high accuracy rates were observed for MNIST and Fashion MNIST datasets, with these values being 99.22% and 99.21%, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nowadays, deep learning methods are widely used in many fields such as robotics, sound processing, medicine and imagery.
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Key Findings & Data:In addition, performance analyzes of deep learning methods were carried out using CNN on 4 different data sets named MNIST, Fashion MNIST, CIFAR-10 and CIFAR-100.
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Conclusion & Impact:With the proposed CNN model, high accuracy rates were observed for MNIST and Fashion MNIST datasets, with these values being 99.22% and 99.21%, respectively.
🔬Design:Lab & Field Experiment
📍Scope:Comprehensive Sample
⚙️Variables:Derin öğrenme · Evrişimli Sinir Ağı · CIFAR-10

Abstract

Nowadays, deep learning methods are widely used in many fields such as robotics, sound processing, medicine and imagery. In this study, studies on image classification and analysis in the literature were examined in detail. In addition, performance analyzes of deep learning methods were carried out using CNN on 4 different data sets named MNIST, Fashion MNIST, CIFAR-10 and CIFAR-100. The structures of the created deep learning models, the parameter values used for training, the layers used, the complexity matrices obtained for the validation data, accuracy and loss graphs are shown in detail. Our study was carried out with a different network structure consisting of 3 convolution layers, 3 batch normalization layers, 2 maxpooling layers, 1 flatten, 2 droupout, 2 dense layers. In addition, the performance of image classification on different data has been increased with our proposed model. At the end of the test, the accuracy results of our study were compared according to various evaluation metrics. For all the data sets used, the best and worst images were determined. With the proposed CNN model, high accuracy rates were observed for MNIST and Fashion MNIST datasets, with these values being 99.22% and 99.21%, respectively.

Özet

Günümüzde derin öğrenme metotları robotik, ses işleme, tıp ve görüntü gibi birçok alanda yaygın kullanılmaktadır. Bu çalışmada literatürde yapılan görüntü sınıflandırma ve analizine yönelik çalışmalar detaylı bir şekilde incelenmiştir. Ayrıca yapılan çalışmada CNN kullanılarak derin öğrenme metotlarının MNIST, Fashion MNIST, CIFAR-10 ve CIFAR-100 ismindeki 4 farklı veri seti üzerinde performans analizleri yapılmıştır. Oluşturulan derin öğrenme modellerinin yapıları, eğitim için kullanılan parametre değerleri, kullanılan katmanlar, doğrulama verileri için elde edilen karmaşıklık matrisleri, doğruluk ve kayıp grafikleri ayrıntılı olarak gösterilmiştir. Çalışmamız 3 adet konvolüsyon katmanı, 3 adet batch normalizasyon katmanı, 2 adet maxpooling katmanı, 1 flatten, 2 droupout, 2 dense katmanından oluşan farklı bir ağ yapısı ile gerçekleştirilmiştir. Ayrıca önerdiğimiz modelle görüntü sınıflandırılmasının farklı veriler üzerindeki performansı artırılmıştır. Test sonunda çalışmamız çeşitli değerlendirme metriklerine göre doğruluk sonuçları karşılaştırılmıştır. Kullanılan tüm veri setleri için en iyi ve en kötü bulunan görüntüler tespit edilmiştir. Önerilen CNN modeli ile MNIST ve Fashion MNIST veri setleri için yüksek doğruluk oranları gözlemlenmiş olup bu değerler sırasıyla %99.22 ve %99.21’dir.

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Cite This Article

Tiraki, Y., Temurtaş, H., Serttaş, S., Bakır, Ç. (2024). A new Convolutional Neural Network proposal for classifying various images. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 13 Sayı: 1, 262–278. https://doi.org/10.28948/ngumuh.1355726

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