İskemik İnme Lezyon Segmentasyonunda Segmentasyon Ağlarının ve Kayıp Fonksiyonlarının Etkilerinin Analizi

BAYRAM, Ahmet Furkan; DERİN, Alperen; GURKAN, Caglar; BUDAK, Abdulkadir; KARATAŞ, Hakan

doi:10.31590/ejosat.1173070

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Nearly half of the 2 million instances of renal colic treated in emergency rooms in the United States each year have CT scans without contrast. 17 Mannil et al 18 used machine learning (ML) for shock wave lithotripsy. The author sought to determine three-dimensional texture analysis for efficacious shock wave lithotripsy in patients with kidney stones.…”

Section: Related Workmentioning

confidence: 99%

End‐to‐end light‐weighted deep‐learning model for abnormality classification in kidney CT images

Karthikeyan,

Kishore,

Sajin

2024

Int J Imaging Syst Tech

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Kidney disease is a major health problem that affects millions of people around the world. Human kidney problems can be diagnosed with the help of computed tomography (CT), which creates cross‐sectional slices of the organ. A deep end‐to‐end convolutional neural network (CNN) model is proposed to help radiologists detect and characterize kidney problems in CT scans of patients. This has the potential to improve diagnostic accuracy and efficiency, which in turn benefits patient care. Our strategy involves teaching a suggested deep end‐to‐end CNN to distinguish between healthy and diseased kidneys. The recommended CNN is trained using a standard CT image library that has been annotated to show kidney stones, cysts, and tumors. The model can then be used to detect kidney abnormalities in fresh CT scans, which may enhance the effectiveness and speed with which diagnoses are made. A total of 1812 pictures were used, each one a unique cross‐sectional CT scan of the patient. Our model has a detection rate of 99.17% in CT scan validation tests. We employed a different dataset with a total of 5077 normal samples, 3709 cyst samples, 1377 stone samples, and 2283 tumor samples. In tests, our model proved to be 99.68% accurate. The suggested framework has been validated by applying it to the clinical dataset, resulting in 99% accuracy in predictions. As low‐cost and portable CT scanners become more commonplace, the described concept may soon be employed outside of a hospital environment, at the point of treatment, or even in the patient's own home.

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Section: Related Workmentioning

confidence: 99%

End‐to‐end light‐weighted deep‐learning model for abnormality classification in kidney CT images

Karthikeyan,

Kishore,

Sajin

2024

Int J Imaging Syst Tech

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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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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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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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İskemik İnme Lezyon Segmentasyonunda Segmentasyon Ağlarının ve Kayıp Fonksiyonlarının Etkilerinin Analizi

Cited by 3 publications

References 7 publications

End‐to‐end light‐weighted deep‐learning model for abnormality classification in kidney CT images

End‐to‐end light‐weighted deep‐learning model for abnormality classification in kidney CT images

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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