Öz
Brain tumors are an important public health problem worldwide. Early diagnosis of brain tumor is critical for the treatment process. In recent years, the use of deep learning models in the computer environment has made significant progress in brain tumor diagnosis and classification. These models can combine data from different imaging models, providing high accuracy and reliable results. In this study, a study was carried out on the Resnet50 deep learning architecture using MR (magnetic resonance) images for brain tumor classification. Gaussian filtering is applied to reduce the problems in the brain images. Thus, by achieving a high accuracy value, it provides early diagnosis of the disease and contributes to automating the tedious and time-consuming diagnostic processes.This way, tumor diagnoses can be made faster and more consistently.
Anahtar Kelimeler
Kaynakça
- Anon. n.d. “Brain Tumor Mri Classification | Kaggle.” Retrieved July 31, 2023 (https://www.kaggle.com/datasets/mohammedhamdy98/brain-tumor-mri-classification).
- Bhanothu, Yakub, Anandhanarayanan Kamalakannan, and Govindaraj Rajamanickam. 2020. “Detection and Classification of Brain Tumor in MRI Images Using Deep Convolutional Network.” 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020 248–52. doi: 10.1109/ICACCS48705.2020.9074375.
- El-Feshawy, Somaya A., Waleed Saad, Mona Shokair, and Moawad Dessouky. 2021. “Brain Tumour Classification Based on Deep Convolutional Neural Networks.” ICEEM 2021 - 2nd IEEE International Conference on Electronic Engineering. doi: 10.1109/ICEEM52022.2021.9480637.
- Geethanjali, N., V. Pushpalatha, C. Ramya, L. Sandhiya, and S. Subhashri. 2023. “Brain Tumor Detection and Classification Using Deep Learning.” Winter Summit on Smart Computing and Networks, WiSSCoN 2023. doi: 10.1109/WISSCON56857.2023.10133851.
- Kabir, Md Ahasan. 2020. “Early Stage Brain Tumor Detection on MRI Image Using a Hybrid Technique.” 2020 IEEE Region 10 Symposium, TENSYMP 2020 1828–31. doi: 10.1109/TENSYMP50017.2020.9230635.
- KARADAĞ, Batuhan, Ali ARI, and Müge KARADAĞ. 2021. “Derin Öğrenme Modellerinin Sinirsel Stil Aktarımı Performanslarının Karşılaştırılması.” Politeknik Dergisi 24(4):1611–22. doi: 10.2339/POLITEKNIK.885838.
- Kartheeban, K., Kapula Kalyani, Sai Krishna Bommavaram, Divya Rohatgi, Mathur Nadarajan Kathiravan, and S. Saravanan. 2022. “Intelligent Deep Residual Network Based Brain Tumor Detection and Classification.” International Conference on Automation, Computing and Renewable Systems, ICACRS 2022 - Proceedings 785–90. doi: 10.1109/ICACRS55517.2022.10029146.
- KAYA, Buket, and Muhammed ÖNAL. 2021. “A CNN Based Method for Detecting Covid-19 from CT Images.” Bilgisayar Bilimleri (Special):1–10. doi: 10.53070/BBD.990793.
- Kumar, Raj, Dinesh Singh, Anuradha Chug, and Amit Prakash Singh. 2022. “Evaluation of Deep Learning Based Resnet-50 for Plant Disease Classification with Stability Analysis.” Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022 1280–87. doi: 10.1109/ICICCS53718.2022.9788207.
- Poornam, S., and Saravanan Alagarsamy. 2022. “Detection of Brain Tumor in MRI Images Using Deep Learning Method.” 3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022 - Proceedings 855–59. doi: 10.1109/ICESC54411.2022.9885583.
- Zhai, Xiaodong, and Fei Qiao. 2020. “A Deep Learning Model with Adaptive Learning Rate for Fault Diagnosis.” Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020 668–73. doi: 10.1109/DDCLS49620.2020.9275094.
Öz
Beyin tümörleri, dünya genelinde önemli bir halk sağlığı sorunu olarak karşımıza çıkmaktadır. Beyin tümörünün erken teşhisi tedavi süreci için kritik bir öneme sahiptir. Son yıllarda, bilgisayar ortamında derin öğrenme modellerinin kullanımı, beyin tümörü teşhisi ve sınıflandırılmasında önemli bir ilerleme sağlamıştır. Bu modeller farklı görüntüleme modellerinden elde edilen verileri birleştirerek yüksek doğruluk oranları ve güvenilir sonuçlar sağlayabilir. Bu çalışmada beyin tümörlerinin sınıflandırılması için MR (manyetik rezonans) görüntüleri kullanılarak Resnet50 derin öğrenme mimarisi üzerinde çalışma gerçekleştirilmiştir. Beyin görüntülerindeki olumsuzlukları azaltmak için Gauss filtreleme işlemi uygulanmıştır. Böylece yüksek oranda doğruluk değerine ulaşarak hastalığın erken teşhisini sağlayıp yorucu ve zaman alıcı teşhis süreçlerini otomatikleştirilmesine katkı sunulmuştur. Bu sayede tümör teşhisleri daha hızlı ve daha tutarlı bir şekilde yapılabilir.
Anahtar Kelimeler
Kaynakça
- Anon. n.d. “Brain Tumor Mri Classification | Kaggle.” Retrieved July 31, 2023 (https://www.kaggle.com/datasets/mohammedhamdy98/brain-tumor-mri-classification).
- Bhanothu, Yakub, Anandhanarayanan Kamalakannan, and Govindaraj Rajamanickam. 2020. “Detection and Classification of Brain Tumor in MRI Images Using Deep Convolutional Network.” 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020 248–52. doi: 10.1109/ICACCS48705.2020.9074375.
- El-Feshawy, Somaya A., Waleed Saad, Mona Shokair, and Moawad Dessouky. 2021. “Brain Tumour Classification Based on Deep Convolutional Neural Networks.” ICEEM 2021 - 2nd IEEE International Conference on Electronic Engineering. doi: 10.1109/ICEEM52022.2021.9480637.
- Geethanjali, N., V. Pushpalatha, C. Ramya, L. Sandhiya, and S. Subhashri. 2023. “Brain Tumor Detection and Classification Using Deep Learning.” Winter Summit on Smart Computing and Networks, WiSSCoN 2023. doi: 10.1109/WISSCON56857.2023.10133851.
- Kabir, Md Ahasan. 2020. “Early Stage Brain Tumor Detection on MRI Image Using a Hybrid Technique.” 2020 IEEE Region 10 Symposium, TENSYMP 2020 1828–31. doi: 10.1109/TENSYMP50017.2020.9230635.
- KARADAĞ, Batuhan, Ali ARI, and Müge KARADAĞ. 2021. “Derin Öğrenme Modellerinin Sinirsel Stil Aktarımı Performanslarının Karşılaştırılması.” Politeknik Dergisi 24(4):1611–22. doi: 10.2339/POLITEKNIK.885838.
- Kartheeban, K., Kapula Kalyani, Sai Krishna Bommavaram, Divya Rohatgi, Mathur Nadarajan Kathiravan, and S. Saravanan. 2022. “Intelligent Deep Residual Network Based Brain Tumor Detection and Classification.” International Conference on Automation, Computing and Renewable Systems, ICACRS 2022 - Proceedings 785–90. doi: 10.1109/ICACRS55517.2022.10029146.
- KAYA, Buket, and Muhammed ÖNAL. 2021. “A CNN Based Method for Detecting Covid-19 from CT Images.” Bilgisayar Bilimleri (Special):1–10. doi: 10.53070/BBD.990793.
- Kumar, Raj, Dinesh Singh, Anuradha Chug, and Amit Prakash Singh. 2022. “Evaluation of Deep Learning Based Resnet-50 for Plant Disease Classification with Stability Analysis.” Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022 1280–87. doi: 10.1109/ICICCS53718.2022.9788207.
- Poornam, S., and Saravanan Alagarsamy. 2022. “Detection of Brain Tumor in MRI Images Using Deep Learning Method.” 3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022 - Proceedings 855–59. doi: 10.1109/ICESC54411.2022.9885583.
- Zhai, Xiaodong, and Fei Qiao. 2020. “A Deep Learning Model with Adaptive Learning Rate for Fault Diagnosis.” Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020 668–73. doi: 10.1109/DDCLS49620.2020.9275094.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Derin Öğrenme |
| Bölüm | PAPERS |
| Yazarlar | |
| Yayımlanma Tarihi | 18 Ekim 2023 |
| Gönderilme Tarihi | 18 Ağustos 2023 |
| Kabul Tarihi | 17 Ekim 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Sayı: IDAP-2023 |
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