CLASSIFICATION OF BRAIN TUMORS WITH DEEP LEARNING MODELS

Beyza Nur Tüzün; Durmuş Özdemir

Article Details

CLASSIFICATION OF BRAIN TUMORS WITH DEEP LEARNING MODELS

Authors:

Beyza Nur Tüzün | Durmuş Özdemir

Publisher:

Kütahya Dumlupinar University

Main Topics:

Engineering

Journal:

Journal of Scientific Reports-A Issue 054, pp.296-306

Document Type:

Article

Publication Date:

9/2023

Document Language:

English

Document ID (Org No.):

2001293061

pISSN:

2687-6167

Keywords:

Abstract:

 This study aims to present a comparative analysis of existing state-of-the-art deep learning models to identify early detection of brain tumor disease using MRI Magnetic Resonance Imaging images. For this purpose, GoogleNet, Mobilenetv2, InceptionV3, and Efficientnet-b0 deep learning models were coded on the Matlab platform and used to detect and classify brain tumor disease. Classification has been carried out on the common Glioma, Meningioma, and Pituitary brain tumors. The dataset includes 7022 brain MRI images in four different classes, which are shared publicly on the Kaggle platform. The dataset was pre-processed and the models were fine-tuned, and appropriate parameter values were used. When the statistical analysis results of the deep learning models we compared were evaluated, the results of Efficientnet-b0 99.54, InceptionV3 99.47, Mobilenetv2 98.93, and GoogleNet 98.25 were obtained, in the order of success. The study results are predicted to be useful in offering suggestions to medical doctors and researchers in the relevant field in their decision-making processes. In particular, it offers some advantages regarding early diagnosis of the disease, shortening the diagnosis time, and minimizing human-induced errors.

 This study aims to present a comparative analysis of existing state-of-the-art deep learning models to identify early detection of brain tumor disease using MRI Magnetic Resonance Imaging images. For this purpose, GoogleNet, Mobilenetv2, InceptionV3, and Efficientnet-b0 deep learning models were coded on the Matlab platform and used to detect and classify brain tumor disease. Classification has been carried out on the common Glioma, Meningioma, and Pituitary brain tumors. The dataset includes 7022 brain MRI images in four different classes, which are shared publicly on the Kaggle platform. The dataset was pre-processed and the models were fine-tuned, and appropriate parameter values were used. When the statistical analysis results of the deep learning models we compared were evaluated, the results of Efficientnet-b0 99.54, InceptionV3 99.47, Mobilenetv2 98.93, and GoogleNet 98.25 were obtained, in the order of success. The study results are predicted to be useful in offering suggestions to medical doctors and researchers in the relevant field in their decision-making processes. In particular, it offers some advantages regarding early diagnosis of the disease, shortening the diagnosis time, and minimizing human-induced errors.

 This study aims to present a comparative analysis of existing state-of-the-art deep learning models to identify early detection of brain tumor disease using MRI Magnetic Resonance Imaging images. For this purpose, GoogleNet, Mobilenetv2, InceptionV3, and Efficientnet-b0 deep learning models were coded on the Matlab platform and used to detect and classify brain tumor disease. Classification has been carried out on the common Glioma, Meningioma, and Pituitary brain tumors. The dataset includes 7022 brain MRI images in four different classes, which are shared publicly on the Kaggle platform. The dataset was pre-processed and the models were fine-tuned, and appropriate parameter values were used. When the statistical analysis results of the deep learning models we compared were evaluated, the results of Efficientnet-b0 99.54, InceptionV3 99.47, Mobilenetv2 98.93, and GoogleNet 98.25 were obtained, in the order of success. The study results are predicted to be useful in offering suggestions to medical doctors and researchers in the relevant field in their decision-making processes. In particular, it offers some advantages regarding early diagnosis of the disease, shortening the diagnosis time, and minimizing human-induced errors.

 This study aims to present a comparative analysis of existing state-of-the-art deep learning models to identify early detection of brain tumor disease using MRI Magnetic Resonance Imaging images. For this purpose, GoogleNet, Mobilenetv2, InceptionV3, and Efficientnet-b0 deep learning models were coded on the Matlab platform and used to detect and classify brain tumor disease. Classification has been carried out on the common Glioma, Meningioma, and Pituitary brain tumors. The dataset includes 7022 brain MRI images in four different classes, which are shared publicly on the Kaggle platform. The dataset was pre-processed and the models were fine-tuned, and appropriate parameter values were used. When the statistical analysis results of the deep learning models we compared were evaluated, the results of Efficientnet-b0 99.54, InceptionV3 99.47, Mobilenetv2 98.93, and GoogleNet 98.25 were obtained, in the order of success. The study results are predicted to be useful in offering suggestions to medical doctors and researchers in the relevant field in their decision-making processes. In particular, it offers some advantages regarding early diagnosis of the disease, shortening the diagnosis time, and minimizing human-induced errors.

Article Details

CLASSIFICATION OF BRAIN TUMORS WITH DEEP LEARNING MODELS

Authors:

Beyza Nur Tüzün | Durmuş Özdemir

Publisher:

Kütahya Dumlupinar University

Main Topics:

Engineering

Journal:

Journal of Scientific Reports-A Issue 054, pp.296-306

Document Type:

Article

Publication Date:

9/2023

Document Language:

English

Document ID (Org No.):

2001293061

pISSN:

2687-6167

Deep Learning

Brain Tumor Detection

Image Classification

MRI processing

Issues

Article Details

CLASSIFICATION OF BRAIN TUMORS WITH DEEP LEARNING MODELS

Authors: Beyza Nur Tüzün | Durmuş Özdemir

Publisher: Kütahya Dumlupinar University

Main Topics: Engineering

Journal: Journal of Scientific Reports-A Issue 054, pp.296-306

Document Type: Article

Publication Date: 9/2023

Document Language: English

Document ID (Org No.): 2001293061

pISSN: 2687-6167

Deep Learning

Brain Tumor Detection

Image Classification

MRI processing

Issues

Authors:

Beyza Nur Tüzün | Durmuş Özdemir

Publisher:

Kütahya Dumlupinar University

Main Topics:

Engineering

Journal:

Journal of Scientific Reports-A Issue 054, pp.296-306

Document Type:

Article

Publication Date:

9/2023

Document Language:

English

Document ID (Org No.):

2001293061

pISSN:

2687-6167