A Convolutional Neural Network Application for Predicting the Locating of Squamous Cell Carcinoma in the Lung

Noğay, Hidir; Akıncı, Tahir Çetin

doi:10.17694/bajece.455132

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Medical image registration with segmentation is very important for monitoring glioblastomas growth during therapy as well as glioblastoma tumor volume measurement. Since the year of 2000, a growing interest and application of medical image processing can be seen from the large number of scientific papers [26][27][28][29]. See Figure 1 for frequency of publications in medical image registration field between years 2000 to 2014.…”

Section: B Literature Surveymentioning

confidence: 99%

Consistency and Comparison of Medical Image Registration-Segmentation and Mathematical Model for Glioblastoma Volume Progression

Irmak¹

2020

Balkan Journal of Electrical and Computer Engineering

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Tumor volume progression analysis and tumor volume measurement are very common tasks in cancer research and image processing fields. Tumor volume measurement can be carried out in two ways. The first way is to use different mathematical formulas and the second way is to use image registration method. In this paper, using 3D medical image registration-segmentation algorithm, multiple scans of MR images of a patient who has brain tumor are registered with different MR images of the same patient acquired at a different time so that growth of the tumor inside the patient's brain can be investigated. Tumor volume progression analysis and tumor volume measurement are performed using image registration technique and the results are compared with the results of tumor volume measurement by mathematical formulas. For the first patient, grown brain tumor volume is found to be 10345 mm³, diminished brain tumor volume is found to be 15278 mm³ and unchanged brain tumor volume is found to be 20876 mm³. Numerical results obtained by image registration model proves that medical imageregistration method is not only between the true ranges but also is very close to the best mathematical formula. Medical image registration-segmentation are implemented to 19 patients and satisfactory results are obtained The results are compared with the results obtained from mathematical methods. An advantageous point of medical image registration-segmentation method over mathematical models for brain tumor investigation is that grown, diminished, and unchanged brain tumor parts of the patients are investigated and computed on an individual basis in a threedimensional (3D) manner within the time.

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Section: B Literature Surveymentioning

confidence: 99%

Consistency and Comparison of Medical Image Registration-Segmentation and Mathematical Model for Glioblastoma Volume Progression

Irmak¹

2020

Balkan Journal of Electrical and Computer Engineering

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“…CNN is a type of artificial neural network (ANN) specialized to handle multi-dimensional, large data. Convolutional networks are neural networks that use at least one layer of convolution processing instead of general matrix multiplication [25][26][27].…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

Lip Reading Using Convolutional Neural Networks with and without Pre-Trained Models

Ozcan

Baştürk

2019

Balkan Journal of Electrical and Computer Engineering

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Lip reading has become a popular topic recently. There are widespread literature studies on lip reading in human action recognition. Deep learning methods are frequently used in this area. In this paper, lip reading from video data is performed using self designed convolutional neural networks (CNNs). For this purpose, standard and also augmented AvLetters dataset is used in train and test stages. To optimize network performance, minibatchsize parameter is also tuned and its effect is investigated. Additionally, experimental studies are performed using AlexNet and GoogleNet pre-trained CNNs. Detailed experimental results are presented.

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Derin evrişimli sinir ağı modellerinin açık kaynak kodlu yazılım platformlarında tasarımının değerlendirilmesi

Selçuk

Akıncı

Şeker

2021

İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Derin evrişimli sinir ağları, iki boyutlu verilerin kullanıldığı, en popüler ve en yaygın derin öğrenme yöntemlerinden birisidir. Özellikle lisans ve lisansüstü öğrencilerin derin öğrenme yöntemlerini özgürce uygulayabilecekleri ve geliştirebilecekleri yeni derin öğrenme modelleri tasarlayabilecekleri, bu konudaki deneyimlerini arttırabilecekleri ortamlara maliyetsiz ve kolayca ulaşabilmeleri, bu gençlerin insanlığa ve bilime hizmet edebilecek bilgi, beceri ve deneyime sahip olmaları açısından çok önemlidir. Açık kaynak kodlu yazılım platformları eğer üniversitelerde ders olarak okutulursa ve öğrencilerin öğrencilik dönemleri boyunca eğitilebilecekleri bir ortama kavuşmaları açısından son derece büyük avantaja sahiptir. Ne var ki günümüzde üniversiteler MATLAB gibi ticari yazılımların lisansını aldıklarında araştırmacıların ulaşabildiği ancak öğrencilerin ulaşamadığı derin öğrenme uygulama ortamları ortaya çıkmaktadır. MATLAB derin öğrenme uygulamalarının gerçekleştirilmesi açısından maliyetli olması dışında önemli bir dezavantajı olmayan bir kapalı kaynak kodlu ticari bir yazılımdır. Bu çalışmada derin evrişimsel sinir ağı modellerinin açık kaynak kodlu yazılım platformlarında tasarımı kaynak araştırması yapılarak ele alınmış ve MATLAB ile kıyaslanmıştır. Açık kaynak kodlu yazılım platformları ile DESA uygulamalarının kolay ulaşılabilir olmasını sağlamak ve gençler arasında popülaritesinin arttırılabilmesi için üniversitelerin müfredat programlarına ders olarak konulması gerekliliği sonucuna varılmıştır.

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A Convolutional Neural Network Application for Predicting the Locating of Squamous Cell Carcinoma in the Lung

Cited by 5 publications

References 10 publications

Consistency and Comparison of Medical Image Registration-Segmentation and Mathematical Model for Glioblastoma Volume Progression

Consistency and Comparison of Medical Image Registration-Segmentation and Mathematical Model for Glioblastoma Volume Progression

Lip Reading Using Convolutional Neural Networks with and without Pre-Trained Models

Derin evrişimli sinir ağı modellerinin açık kaynak kodlu yazılım platformlarında tasarımının değerlendirilmesi

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