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Journals / Uluslararası Stratejik Boyut Dergisi / 2025 / Cilt: 4 Sayı: 2

DEEP LEARNING-BASED IMAGE ANALYSIS IN DIFFERENT TYPES OF CANCER: A LITERATURE REVIEW

FARKLI KANSER TÜRLERİNDE DERİN ÖĞRENMEYE DAYALI GÖRÜNTÜ ANALİZİ: BİR LİTERATÜR TARAMASI

Ejder Ertürkmen(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ)0000-0001-5961-6326
Hanifi Çam(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, TEKNİK BİLİMLER MESLEK YÜKSEKOKULU)
Ali Öter(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, TEKNİK BİLİMLER MESLEK YÜKSEKOKULU)0000-0002-9546-0602
Pages
105–120
Publication Date
2025-10-30
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AI Summary & Key Takeaways

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

The study highlights the current state of deep learning-based methods in medical image analysis and the challenges encountered within an interdisciplinary framework.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This research systematically reviews deep learning (DL)-based medical image analysis methods used in common types of cancer, such as breast, lung, brain, skin, prostate, and colon cancer.
📊
Key Findings & Data:As part of the literature review, recent studies published over the past five years were compared and analyzed in terms of the methods used (CNN, U-Net, ResNet, etc.), the datasets employed (DDSM, LIDC-IDRI, BraTS, ISIC, etc.), and the performance metrics achieved.
💡
Conclusion & Impact:The study highlights the current state of deep learning-based methods in medical image analysis and the challenges encountered within an interdisciplinary framework.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Derin öğrenme · Tıbbi görüntü analiz · Kanser teşhisi

Abstract

This research systematically reviews deep learning (DL)-based medical image analysis methods used in common types of cancer, such as breast, lung, brain, skin, prostate, and colon cancer. As part of the literature review, recent studies published over the past five years were compared and analyzed in terms of the methods used (CNN, U-Net, ResNet, etc.), the datasets employed (DDSM, LIDC-IDRI, BraTS, ISIC, etc.), and the performance metrics achieved. Deep learning architectures offer high accuracy rates in tasks such as image classification, segmentation, lesion detection, and prognostic modeling; their performance is enhanced through transfer learning, attention mechanisms, and multi-task learning strategies. On the other hand, various limitations in areas such as model explainability, data security, ethical oversight, and clinical integration are also noteworthy. The study highlights the current state of deep learning-based methods in medical image analysis and the challenges encountered within an interdisciplinary framework.

Özet

Bu çalışma, meme, akciğer, beyin, cilt, prostat ve kolon kanseri gibi yaygın kanser türlerinde kullanılan derin öğrenme (DÖ) tabanlı tıbbi görüntü analiz yöntemlerini sistematik bir bakış açısıyla incelemektedir. Literatür taraması kapsamında, son beş yılda yayınlanan güncel çalışmalar; kullanılan yöntemler (CNN, U-Net, ResNet vb.), veri setleri (DDSM, LIDC-IDRI, BraTS, ISIC vb.) ve elde edilen başarı metrikleri açısından karşılaştırmalı olarak analiz edilmiştir. Derin öğrenme mimarileri, görüntü sınıflandırma, segmentasyon, lezyon tespiti ve prognostik modelleme gibi görevlerde yüksek doğruluk oranları sunmakta; transfer öğrenme, attention mekanizmaları ve çok görevli öğrenme stratejileriyle performansları artırılmaktadır. Öte yandan, model açıklanabilirliği, veri güvenliği, etik denetim ve klinik entegrasyon gibi başlıklarda çeşitli sınırlılıklar da dikkat çekmektedir. Çalışma, derin öğrenme temelli yöntemlerin tıbbi görüntü analizinde mevcut durumu ve karşılaşılan zorlukları disiplinler arası bir çerçevede ortaya koymaktadır.

🔖

Cite This Article

Ertürkmen, E., Çam, H., Öter, A. (2025). DEEP LEARNING-BASED IMAGE ANALYSIS IN DIFFERENT TYPES OF CANCER: A LITERATURE REVIEW. Uluslararası Stratejik Boyut Dergisi, Cilt: 4 Sayı: 2, 105–120. https://doi.org/10.63742/ustbd.1744186

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