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Dergiler / Gazi University Journal of Science Part A: Engineering and Innovation / 2025 / Volume: 12 Issue: 4

An Optimized and Attention-Enhanced Convolutional Neural Network for Accurate Breast Cancer Diagnosis

Ragheed Mokhtar(TOKAT GAZIOSMANPASA UNIVERSITY)0009-0009-2252-4116
Mahir Kaya(Tokat Gaziosmanpaşa Üniversitesi, Bilgisayar Mühendisliği Bölümü)0000-0001-9182-271X
Sayfa
—
Yayın Tarihi
2025-12-31
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The proposed model, interpretable through Grad-CAM, effectively focuses on disease-related regions and enables specialists to make fast and reliable decisions.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Breast cancer continues to be one of the most prevalent as well as life-threatening malignancies because it affects women globally.
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Temel Bulgular & Veri:The proposed CNN architecture integrates state-of-the-art deep learning techniques that include hierarchical feature extraction, attention mechanisms, batch normalization, dropout regularization, along with adaptive learning rates.
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Sonuç & Katkı:The proposed model, interpretable through Grad-CAM, effectively focuses on disease-related regions and enables specialists to make fast and reliable decisions.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Breast Cancer · Breast Ultrasound Imaging · Convolutional Neural Networks

Abstract

Breast cancer continues to be one of the most prevalent as well as life-threatening malignancies because it affects women globally. Ultrasound images get diagnostic interpretation that is customarily variable, with limitations in sensitivity and specificity. For breast ultrasound image classification, we propose a novel fully optimized Convolutional Neural Network (CNN) architecture. This study's classification includes three clinically important categories: normal, malignant, and benign. We used the Breast Ultrasound Images (BUSI) dataset, and we tackled intrinsic issues like small dataset size, class imbalance, and inter-class similarity via a diverse augmentation pipeline. The proposed CNN architecture integrates state-of-the-art deep learning techniques that include hierarchical feature extraction, attention mechanisms, batch normalization, dropout regularization, along with adaptive learning rates. The best-performing model reached a test accuracy of 99.33%, demonstrating the effectiveness of the proposed approach. This performance gain was obtained through systematic hyperparameter optimization involving the number of convolutional layers, learning rates, batch sizes, and input image resolutions. Although trained without pre-existing knowledge, the model showed similar performance when evaluated against popular transfer learning models like MobileNetV2, VGG16, VGG19, InceptionV3, ResNet50, EfficientNetB0, DenseNet121, and Xception. The proposed model, interpretable through Grad-CAM, effectively focuses on disease-related regions and enables specialists to make fast and reliable decisions.

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Bu Makaleye Atıf Yap

Mokhtar, R., Kaya, M. (2025). An Optimized and Attention-Enhanced Convolutional Neural Network for Accurate Breast Cancer Diagnosis. Gazi University Journal of Science Part A: Engineering and Innovation, Volume: 12 Issue: 4. https://doi.org/10.54287/gujsa.1804559

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Kaynakça (27)

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