Derin Evrişimli Sinir Ağlarını Kullanarak MRG Modalitesinde Alzheimer Hastalığının Sınıflandırılması ve Segmentasyonu

The prevalence of brain tumor is quite high. Brain tumor causes critical diseases. Also, brain tumor causes a variety of symptoms in most people. This study aims to segmentation of the tumor in the brain. For this purpose, state-of-art architectures, such as UNet, Attention UNet, Residual UNet, Attention Residual UNet, Residual UNet++, Inception UNet, LinkNet, and SegNet were used for segmentation. 592 magnetic resonance (MR) images were utilized in the training and testing of segmentation architectures. In the comparative analysis, Attention UNet achieved the best predictive performance with a 0.886 dice score, 0.795 IoU score, 0.881 sensitivity, 0.993 specificity, 0.891 precision, and 0.986 accuracy.

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Convolutional Neural Networks for MRI-Based Brain Tumor Segmentation: A Comparative Analysis of State-of-the-Art Segmentation Networks

BAYRAM

GURKAN

BUDAK³

et al. 2022

Turkish Journal of Forecasting

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Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality

BAYRAM

GURKAN

BUDAK³

et al. 2022

Turkish Journal of Forecasting

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The pancreas is one of the vital organs in the human body. Early diagnosis of a disease in the pancreas is critical. In this way, the effects of pancreas diseases, especially pancreatic cancer on the person are decreased. With this purpose, artificial intelligence-assisted pancreatic cancer segmentation was performed for early diagnosis in this paper. For this aim, several state-of-the-art segmentation networks, UNet, LinkNet, SegNet, SQ-Net, DABNet, EDANet, and ESNet were used in this study. In the comparative analysis, the best segmentation performance has been achieved by SQ-Net. SQ-Net has achieved a 0.917 dice score, 0.847 IoU score, 0.920 sensitivity, 1.000 specificity, 0.914 precision, and 0.999 accuracy. Considering these results, an artificial intelligence-based decision support system was created in the study.

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Derin Evrişimli Sinir Ağlarını Kullanarak MRG Modalitesinde Alzheimer Hastalığının Sınıflandırılması ve Segmentasyonu

Cited by 2 publications

References 8 publications

Convolutional Neural Networks for MRI-Based Brain Tumor Segmentation: A Comparative Analysis of State-of-the-Art Segmentation Networks

Convolutional Neural Networks for MRI-Based Brain Tumor Segmentation: A Comparative Analysis of State-of-the-Art Segmentation Networks

Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality

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