Programmed Multi-Classification of Brain Tumor Images Using Deep Neural Network

Nagaraj, P.; Muneeswaran, V.; Reddy, L. Veera; Upendra, Parvathaneni; Reddy, M. Vishnu Vardhan

doi:10.1109/iciccs48265.2020.9121016

Cited by 58 publications

(11 citation statements)

References 10 publications

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“…The purpose of the pooling layer is to make the image smaller by reducing the number of parameters when the images are very large. This operation reduces the dimensionality of each map but preserves important information 29 . Generally, two types of pooling methods are used.…”

Section: Methodsmentioning

confidence: 99%

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A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

Cinar

Kaya

2022

Int J Imaging Syst Tech

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Brain tumor is a disease that seriously threatens human health and can often be treated with risky surgeries. Experts detect brain tumor with high resolution magnetic resonance (MR) images. However, the expected accuracy value could not be reached in the studies carried out so far. The aim of this study is to develop a new approach for detecting brain tumor types using MR images. In the proposed approach, it is designed a CNN-based neural network from scratch. The results of the method were compared with existing networks. The proposed approach detected glioma tumors with 99.64%, meningiomas tumor with 96.53%, pituitary tumors with 98.39% and an average of 98.32% accuracy. The developed CNN based model is also compared with deep CNN models such as ResNet50, VGG19, DensetNet121 and InceptionV3, which are operated by transfer learning method. The results show that the proposed approach outperforms other deep neural networks.

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Section: Methodsmentioning

confidence: 99%

“…In this study, a network obtained by developing a classical CNNbased network is proposed. 28 The proposed model is shown in Figure 4.…”

Section: Proposed Cnn Architecturementioning

confidence: 99%

A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

Cinar

Kaya

2022

Int J Imaging Syst Tech

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“…Nagaraj et al 59 proposed a CNN‐based model for the multi‐classification of brain tumor. The three developed methods classified the MR images into various types and further classified them into different grades.…”

Section: Literature Reviewmentioning

confidence: 99%

A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images

Gull

Akbar

Naqi

2023

Int J Imaging Syst Tech

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Brain tumor is the 10th major cause of death among humans. The detection of brain tumor is a significant process in the medical field. Therefore, the objective of this research work is to propose a fully automated deep learning framework for multistage classification. Besides, this study focuses on to develop an efficient and reliable system using a convolutional neural network (CNN). In this study, the fast bounding box technique is used for segmentation. Moreover, the CNN layers‐based three models are developed for multistage classification through magnetic resonance images on three publicly available datasets. The first dataset is obtained from Kaggle Repository (Dataset‐1), the second dataset is known as Figshare (Dataset‐2), and the third dataset is called REMBRANDT (Dataset‐3) to classify the MR images into different grades. Different augmentation techniques are applied to increase the data size of MR images. In pre‐processing, the proposed models achieved higher Peak Signal‐to‐Noise ratio to remove noise. The first proposed deep CNN framework mentioned as Classification‐1 has obtained 99.40% accuracy, which classified MR images into two classes, that is (i) normal and (ii) abnormal, while the second proposed CNN framework mentioned as Classification‐2 has obtained 97.78% accuracy, which classified brain tumor into three types, which are meningioma, glioma, and pituitary. Similarly, the third developed CNN framework mentioned as Classification‐3 has obtained 98.91% accuracy that further classified MR images of tumors into four different classes as: Grade I, Grade II, Grade III, and Grade IV. The results demonstrate that the proposed models achieved better performance on three large and diverse datasets. The comparison of obtained outcomes shows that the developed models are more efficient and effective than state‐of‐the‐art methods.

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“…Similarly in 2020, X. Liu et al [15] 128 of glioblastoma tumor by utilizing CNN method to prove that CNN method was deemed reliable and accurate in the tumor classification process. In 2020, P. Nagaraj et al [16] performed a classification of Meningioma, Glioma, and Pituitary brain tumors with 3064 images. In this study, the 2 experiments were undertaken with the first experiment to classify the brain tumor status and the second experiment to classify the tumor grade.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

Minarno

Mandiri

Munarko

et al. 2021

KINETIK

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Brain tumor has been acknowledged as the most dangerous disease through all its circles. Early identification of tumor disease is considered pivotal to identify the spread of brain tumors in administering the appropriate treatment. This study proposes a Convolutional Neural Network method to detect brain tumor on MRI images. The 3264 datasets were undertaken in this study with detailed images of Glioma tumor (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and other with no-tumors (500 images). The application of CNN method combined with Hyperparameter Tuning is proposed to achieve optimal results in classifying the brain tumor types. Hyperparameter Tuning acts as a navigator to achieve the best parameters in the proposed CNN model. In this study, the model testing was conducted with three different scenarios. The result of brain tumor classification depicts an accuracy of 96% in the third model testing scenario.

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Programmed Multi-Classification of Brain Tumor Images Using Deep Neural Network

Cited by 58 publications

References 10 publications

A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images

Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

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