Ön Eğitimli Evrişimsel Sinir Ağı Modellerinde Öznitelik Seçim Algoritmasını Kullanarak Cilt Lezyon Görüntülerinin Sınıflandırılması

Taşçı, Burak

doi:10.35234/fumbd.1077322

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…In addition, these diseases can be detected by artificial intelligence techniques. Moreover, the detection of diseases with OCT, EEG, ECG, and MRI can be accomplished with artificial intelligence techniques [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. In the future, addiction detection studies using artificial intelligence techniques can be done with OCT images.…”

Section: Discussionmentioning

confidence: 99%

OCT Findings in Patients with Methamphetamine Use Disorder

Kaya

Kaya²

2023

JPM

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Purpose: In the present study, the purpose was to examine the results of optical coherence tomography (OCT) measurements in patients diagnosed with methamphetamine use disorder (MUD) by comparing them with healthy controls. Materials and Methods: A total of 114 eyes were evaluated in this study (27 patients and 30 control group participants). After detailed biomicroscopic examinations of all participants by the same ophthalmologist, both eyes were evaluated by OCT. The retinal nerve fiber layer thickness (RNFL) and macular thickness were calculated from OCT. Results: No statistically significant differences were detected between the demographic data of the patient and control groups (p > 0.05). When OCT findings were evaluated, macular thickness and volume were not different between the groups (p > 0.05). With respect to RNFL, the left eye superior, inferior, temporal, and nasal quadrants, as well as the left eye’s total measurements were found to be thicker than those of controls (p < 0.05). In both eyes, the left eye nasal quadrant and APIS total score were negatively correlated, the total RNLF measurement of the right eye and APIS motivation subscale score were negatively correlated, central macular thickness and the APIS motivation subscale score were positively correlated, and the APIS substance use characteristics subscale score and left eye temporal quadrant RNLF measurement were positively correlated. Conclusion: Our study is the first to evaluate addiction severity and OCT findings in MUD. However, this study needs to be supported by further studies so that OCT findings, which can be used as an effective method for demonstrating possible neurodegeneration in methamphetamine use disorder, gain importance.

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Section: Discussionmentioning

confidence: 99%

OCT Findings in Patients with Methamphetamine Use Disorder

Kaya

Kaya²

2023

JPM

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“…For this reason, mega voltage devices used in radiotherapy should focus on damaged tissue and it is difficult to preserve intact tissue. It is not possible to give the same dose of radiation to entire tumor area with mega voltage radiotherapy [9].…”

Section: External Beam Radiotherapy Methodsmentioning

confidence: 99%

Brachytherapy Method Using Individualized Applicators to Increase Efficiency in Skin Tumors Treatment

Aslan,

Ünlü,

Emir

et al. 2024

EJRnD

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Brachytherapy method using individualized applicators to increase efficiency in skin tumors treatment is presented. Skin tumors are one of the most common types of cancer in the world. Nonmelanoma skin basal cell and squamous cell skin carcinomas, known as NMSCs, are the most common cancers. The main purpose of this work is to comprehend basic treatment methods of skin tumors. The other purpose is to emphasize advantages and impact of the brachytherapy method using individualized applicators. Earlier skin tumor treatment methods are surgery and external (electron beam) radiotherapy. However, the brachytherapy method not widely used in skin tumor treatment process provides more efficient results due to the usage of individualized applicators specially produced for patients. Conclusively, individualized applicators provides more benefits and the fact that these applicators are improved much facilitates treatment process.

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“…Convolution filters found in deep learning architectures eliminate the requirement for manual feature extraction. Because of this, deep learning-based studies have excelled at several tasks involving the classification of medical images [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. A pre-trained CNN-based model was utilized by Lu et al [21] to identify brain cancers in MRI images.…”

Section: Introductionmentioning

confidence: 99%

Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images

Demir

Ari

Demir

2023

FUJECE

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One of the most dangerous diseases in the world is a brain tumor. A brain tumor destroys healthy tissue in the brain and then multiplies abnormally, causing increased internal pressure in the skull. This can lead to death if not diagnosed early. Magnetic Resonance Imaging (MRI) is a diagnostic method that is frequently used in soft tissues and gives successful results. In this study, a brain tumor was automatically detected from MR images. For feature extraction, a pre-trained Convolutional Neural Network (CNN) model named MobilenetV2 was used. Then, the ReliefF algorithm was used for feature selection. The features extracted with MobileNetV2 and the features selected with the ReliefF algorithm are given separately to the classifiers and the system performance is tested. As a result of experimental studies, it was seen that the highest performance was obtained with the combination of MobileNetV2 feature extraction, ReliefF algorithm feature selection, and KNN classifier.

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Ön Eğitimli Evrişimsel Sinir Ağı Modellerinde Öznitelik Seçim Algoritmasını Kullanarak Cilt Lezyon Görüntülerinin Sınıflandırılması

Cited by 4 publications

References 21 publications

OCT Findings in Patients with Methamphetamine Use Disorder

OCT Findings in Patients with Methamphetamine Use Disorder

Brachytherapy Method Using Individualized Applicators to Increase Efficiency in Skin Tumors Treatment

Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images

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