GÖRÜNTÜ BÖLÜTLEME ve GÖRÜNTÜ BENZETİMİ YÖNTEMLERİ YARDIMI ile HASTALIĞIN TEŞHİS ve TEDAVİ SONRASI SÜREÇLERİNİN DESTEKLENMESİ: KERATOKONUS ÖRNEĞİ

Kaya, Hilal; Çavuşoğlu, Abdullah; Çakmak, Hasan Basri; Şen, Baha; Delen, Dursun

doi:10.17341/gummfd.85749

Cited by 8 publications

(2 citation statements)

References 28 publications

(25 reference statements)

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“…A watershed segmentation technique was used for the effective segmentation of cranial MRIs. This technique was used for determination of tumors [20]. Watershed built on the terms of watershed lines is connected to topography and water source basin.…”

Section: Watershed Segmentation and Morphological Processmentioning

confidence: 99%

Deep learning based brain tumor classification and detection system

Arı¹,

Hanbay²

2018

Turk J Elec Eng & Comp Sci

182

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The brain cancer treatment process depends on the physician's experience and knowledge. For this reason, using an automated tumor detection system is extremely important to aid radiologists and physicians to detect brain tumors. The proposed method has three stages, which are preprocessing, the extreme learning machine local receptive fields (ELM-LRF) based tumor classification, and image processing based tumor region extraction. At first, nonlocal means and local smoothing methods were used to remove possible noises. In the second stage, cranial magnetic resonance (MR) images were classified as benign or malignant by using ELM-LRF. In the third stage, the tumors were segmented. The purpose of the study was using only cranial MR images, which have a mass, in order to save the physician's time. In the experimental studies the classification accuracy of cranial MR images is 97.18%. Evaluated results showed that the proposed method's performance was better than the other recent studies in the literature. Experimental results also proved that the proposed method is effective and can be used in computer aided brain tumor detection.

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Section: Watershed Segmentation and Morphological Processmentioning

confidence: 99%

Deep learning based brain tumor classification and detection system

Arı¹,

Hanbay²

2018

Turk J Elec Eng & Comp Sci

182

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“…The survival time is between 14 months and 12 years depending on the stage of the brain cancer at the diagnosis time. Early diagnosis of tumor type is important in lengthening this period [1,2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Kutlu

Avcı

2019

Sensors

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Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN–DWT–LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN–DWT–LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Fırat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN–DWT–LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.

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Optimization of CBCT data with image processing methods and production with fused deposition modeling 3D printing

Sayin,

Aksoy,

Özsoy

2023

Med Biol Eng Comput

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GÖRÜNTÜ BÖLÜTLEME ve GÖRÜNTÜ BENZETİMİ YÖNTEMLERİ YARDIMI ile HASTALIĞIN TEŞHİS ve TEDAVİ SONRASI SÜREÇLERİNİN DESTEKLENMESİ: KERATOKONUS ÖRNEĞİ

Abstract: Korneanın üç boyutlu görüntülenmesi  Hastalığın ve iyileşme sürecinin izlenmesi için bir destek uygulaması  Görüntü bölütleme ve görüntü işleme yöntemleri

Cited by 8 publications

References 28 publications

Deep learning based brain tumor classification and detection system

Deep learning based brain tumor classification and detection system

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Optimization of CBCT data with image processing methods and production with fused deposition modeling 3D printing

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