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Journals / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 4

Precision in Breast Cancer Detection with Advanced Deep Learning for Lesion Localization and Classification

Süleyman Uzun(SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0001-8246-6733
A F M Suaib Akhter(SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, UYGULAMALI BİLİMLER FAKÜLTESİ)0000-0002-2675-1684
Salim Ceyhan(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0000-0003-0274-6175
Yavuz Biçici(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0000-0002-1711-2418
Zafer Serin(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0000-0002-5213-8517
Al-sakib Khan Pathan(United International University)0000-0001-6572-3451
Pages
—
Publication Date
2026-09-30
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AI Summary & Key Takeaways

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

These results highlight the effectiveness of the proposed approach in addressing challenges in breast cancer diagnosis.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Breast cancer is a global threat that mainly pertains to women.
📊
Key Findings & Data:The need for detection at an early stage is crucial, but accurate detection of lesions due to breast cancer is still a huge challenge worldwide.
💡
Conclusion & Impact:These results highlight the effectiveness of the proposed approach in addressing challenges in breast cancer diagnosis.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Breast cancer · Mammography · Machine Learning

Abstract

Breast cancer is a global threat that mainly pertains to women. The need for detection at an early stage is crucial, but accurate detection of lesions due to breast cancer is still a huge challenge worldwide. This paper deals with the need for accurate detection and classification of lesions in mammogram images. A comprehensive study has been performed using the most advanced deep learning (DL) techniques. The study utilizes the DDSM dataset from the University of South Florida for mammographic image analysis. Among the models, the Faster R-CNN + ResNet-50 achieved 99.63% accuracy in lesion detection, while Inception V2 achieved 99.41% accuracy in tumor classification. These results highlight the effectiveness of the proposed approach in addressing challenges in breast cancer diagnosis.

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Cite This Article

Uzun, S., Akhter, A. F. M. S., Ceyhan, S., Biçici, Y., Serin, Z., Pathan, A. K. (2026). Precision in Breast Cancer Detection with Advanced Deep Learning for Lesion Localization and Classification. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 4. https://doi.org/10.35377/saucis...1711862

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