Açıklanabilir Evrişimsel Sinir Ağları ile Beyin Tümörü Tespiti

Orman, Abdullah; Köse, Utku; Yi̇ği̇t, Tuncay

doi:10.31202/ecjse.924446

Cited by 3 publications

(2 citation statements)

References 79 publications

(64 reference statements)

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“…CNN networks are advantageous because they require less preprocessing. It has applications in many different fields such as image recognition, image classification and natural language processing (Orman et al ,2021;Türk et al, 2020;Küçük, 2021).By extracting the properties of the data given to the architecture, correlations within the data are made and classified. CNN networks consist of three main parts; the Convolutional Layer, Pooling Layer and Fully Connected layer.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

KAHRAMAN,

CİVELEK

2023

UMAG

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Abstract The new virus disease (COVID-19) first came to China towards the end of December 2019 and became a pandemic all over the world. The disease caused a large number of people to be infected and die. Rapid diagnosis of the disease is of great importance in controlling transmission. A computed Tomography device provides successful results in the diagnosis of COVID-19 disease. In this study, two-class (COVID-19 and normal) data sets were created from 7200 lung Computed Tomography images diagnosed between March 2020 and November 2020 in a private hospital with the help of specialist physicians. Verification and testing processes were carried out on Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) algorithms from Machine Learning algorithms, and ResNet-50, DenseNet-201, InceptionResNetV2, Inceptionv3, VGG-16, Xception architectures from Deep Learning models. As a result of the studies, the DenseNet-201 architecture obtained the highest result from deep learning models with %99,35 training and test %98,75 accuracy rates, respectively. ANN %97,6, KNN %97,4 and SVM %96,9 accuracy rates were obtained from machine learning.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

KAHRAMAN,

CİVELEK

2023

UMAG

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“…A convolutional neural network (CNN) is one of the most widely used deep learning approaches. In the literature, there are studies carried out in many different fields, including in the field of agriculture [1] and in the field of health [2], using CNN. Standard CNN models are well-suited to extracting generic descriptions from a dataset and categorizing the input data, but they are insufficient for object detection networks to locate specified objects on an image [3].…”

Section: Introductionmentioning

confidence: 99%

Mask R-CNN kullanarak COVID-19 için Yüz Maskesi Takma Durumunun Tespiti

BATTAL>

Tuncer

2022

ECJSE

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Tüm dünyayı etkisi altına alan COVID-19 salgını nedeniyle ülkeler insanların yüz maskesi takmasını zorunlu hale getirdi. Çünkü maske takmak virüsün bulaşma riskini azaltmak için en etkili yöntemlerden biri olarak kabul edilmektedir. Ancak insanların maske takıp takmadığını manuel olarak kontrol etmek zordur. Bu çalışmada derin bir sinir ağı kullanılarak kalabalık ortamlarda her türlü yüz maskesini algılayan bir modelin geliştirilmesi amaçlanmıştır. Derin öğrenme algoritmalarından biri olan ve nesne tespiti için kullanılan Mask R-CNN, insanların maske durumlarını tespit etmek ve sınıflandırmak için kullanıldı. Önerilen derin öğrenme modeli, üç sınıf (maskeli, maskesiz, yanlış maske kullanımı) içeren 853 görüntüden oluşan bir veri seti kullanılarak k-kat çapraz doğrulama ile eğitildi ve test edildi. Omurga mimarisi olarak ResNet101 seçildi ve COCO modeli kullanılarak transfer öğrenmesi gerçekleştirildi. Önerilen Mask R-CNN modeli, %83'lük bir mAP, %90'lık bir mAR ve %86'lık bir F1 puanına ulaşmıştır. Bu sonuçlar önerilen modelin maske tespitinde başarılı olduğunu ortaya koymaktadır.

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Classification of Environmental Sounds With Deep Learning

Aksoy

USTA

KARADAĞ

et al. 2022

Advances in Artificial Intelligence Research

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Today, with the development of technology, environmental destruction is increasing day by day. For this reason, it is inevitable to take different measures to prevent the damage caused by environmental destruction. It is possible to prevent environmental damage by identifying the sounds that harm the environment and transferring them to the relevant units. In the study carried out, a data set of saw, rain, lightning, bark and broom sound data obtained from open access websites was created. Rain, barking and broom sounds in the data set were determined as the sounds that do not harm the environment, while saw and lightning were determined as the data set that harms the environment. The dataset was classified using VGG-13BN, ResNet-50 and DenseNet-121 deep learning architectures. When used, all three deep learning accuracy are due to over 95% study. Among these models, the VGG-13 BN model emerged as the most successful model with an accuracy rate of 99.72%.

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Açıklanabilir Evrişimsel Sinir Ağları ile Beyin Tümörü Tespiti

Cited by 3 publications

References 79 publications

Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

Mask R-CNN kullanarak COVID-19 için Yüz Maskesi Takma Durumunun Tespiti

Classification of Environmental Sounds With Deep Learning

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