Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

Minarno, Agus Eko; Mandiri, Mochammad Hazmi Cokro; Munarko, Yuda; Hariyady, Hariyady

doi:10.22219/kinetik.v6i2.1219

Cited by 25 publications

(12 citation statements)

References 22 publications

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“…A deep network is developed for brain tumor classification on the Kaggle dataset, and it provides an accuracy of 97.87% [ 67 ]. The CNN model is trained on the tuned hyperparameters using the Kaggle dataset, and it gives an accuracy of 96.0% [ 68 ]. GRU, LSTM, RNN, and CNN models are used as base learner with a min-max fuzzy classifier for classification, and this provides an accuracy of 95.24% [ 69 ].…”

Section: Resultsmentioning

confidence: 99%

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Amin

Anjum

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.

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Section: Resultsmentioning

confidence: 99%

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Amin

Anjum

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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“…The results of the accuracy test in the four model scenarios are summarized in Table 14 as follows. In this study, an evaluation of the model was carried out on previous studies [1]. Details from comparison to previous studies are illustrated in Table 15 as follows.…”

Section: Model 3 Scenario Testingmentioning

confidence: 99%

“…In general, brain tumors are divided into two types, namely benign tumors, and malignant tumors [1], the tumor grade range is 1 to 4. Benign tumors based on the rate of growth and the way of spreading are in grades 1 to 2 and do not have the potential to become malignant tumors [6].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet

Wibowo

Fayyadl

Azhar

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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A brain tumor is a lump caused by an imperfect cell turnover cycle in the brain and can affect all ages. Brain tumors have 4 grades, namely grades 1 to 2 are benign tumor grades, and grades 3 to 4 are malignant tumor grades. Therefore, early identification of brain tumor disease is very important in providing appropriate treatment and treatment. This study uses a dataset obtained through the Kaggle website titled Brain Tumor Classification (MRI). The number of data is 3264 images with details of Glioma tumors (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and without tumors (500 images). In this study, there are 4 scenarios with different testers. This study proposes the classification of brain tumors using Hyperparameter Tuning and EfficientNet models on MRI images. The EfficientNet model used is the EfficientNetB0 and EfficientNetB7 models with the architecture used are the input layer, GlobalAveragePooling2D layer, dropout layer, and dense layer as well as adding augmentation data to the dataset to manipulate the data in order to improve the results of the proposed model. After building the model, the results of accuracy, precision, recall, and f1-score will be obtained in each scenario. Accuracy results in Scenario 1 are 91%, scenario 2 is 95% accurate, scenario 3 is 95%, and scenario 4 is 98%.

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“…With a size of 1,350cc, the brain has more than 100 million nerve cells, which control all human activities. The cerebral cortex refers to the outer part of the human brain that processes sensory performance and produces motor and high-level cognitive activities [1]. Therefore, the brain becomes one of the most important organs in supporting human life.…”

Section: Introductionmentioning

confidence: 99%

Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine

Minarno

Kantomo²,

Sumadi³

et al. 2022

JOIV : Int. J. Inform. Visualization

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The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.

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Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

Cited by 25 publications

References 22 publications

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet

Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine

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