A Hybrid CNN-KNN Model for MRI brain Tumor Classification

Srinivas, B.; Rao, G. Sasibhushana

doi:10.35940/ijrte.b1051.078219

Cited by 28 publications

(6 citation statements)

References 9 publications

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“…DenseNet201 comprises one CL, one MPL, three TL, one APL, one FCL, and one SML with 10.2 million trainable parameters. It has also four DBL in which third and fourth DBL have two CL of stride 1 × 1 and stride 3 × 3, respectively [15]. VGG16 comprises thirteen CL, five MPL, three FCL, and one SML with 138 million trainable parameters [16].…”

Section: Brain Tumor Prediction Using Pretrained Cnn Modelsmentioning

confidence: 99%

Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

Sharma

Gupta

et al. 2022

Journal of Sensors

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A brain tumor (BT) is an unexpected growth or fleshy mass of abnormal cells. Depending upon their cell structure they could either be benign (noncancerous) or malign (cancerous). This causes the pressure inside the cranium to increase that may lead to brain injury or death. This causes excessive exhaustion, hinders cognitive abilities, headaches become more frequent and severe, and develops seizures, nausea, and vomiting. Therefore, in order to diagnose BT computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and blood and urine tests are implemented. However, these techniques are time consuming and sometimes yield inaccurate results. Therefore, to avoid such lengthy and time-consuming techniques, deep learning models are implemented that are less time consuming, require less sophisticated equipment, yield results with greater accuracy, and are easy to implement. This paper proposes a transfer learning-based model with the help of pretrained VGG19 model. This model has been modified by utilizing a modified convolutional neural network (CNN) architecture with preprocessing techniques of normalization and data augmentation. The proposed model achieved the accuracy of 98% and sensitivity of 94.73%. It is concluded from the results that proposed model performs better as compared to other state-of-art models. For training purpose, the dataset has been taken from the Kaggle having 257 images with 157 with brain tumor (BT) images and 100 no tumor (NT) images. With such results, these models could be utilized for developing clinically useful solutions that are able to detect BT in CT images.

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Section: Brain Tumor Prediction Using Pretrained Cnn Modelsmentioning

confidence: 99%

Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

Sharma

Gupta

et al. 2022

Journal of Sensors

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“…The proposed RBEBT is compared with seven other state-of-the-art methods, which are 3D-CNN [1], SVM-CNN [2], KNN-CNN [5], 2D-CNN [7], BPNN [11], LVQNN [12], and LRC [13], as shown in Tab. 2.…”

Section: Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

“…In the proposed method, there were two channels to classify the brain tumors. Srinivas et al [5] designed a new model (CNN-KNN) to classify brain tumors. KNN used the features which were extracted by CNN for classification.…”

Section: Introductionmentioning

confidence: 99%

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Zhu¹,

Khan²,

Wang³

et al. 2023

Computers, Materials &Amp; Continua

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Brain tumor refers to the formation of abnormal cells in the brain. It can be divided into benign and malignant. The main diagnostic methods for brain tumors are plain X-ray film, Magnetic resonance imaging (MRI), and so on. However, these artificial diagnosis methods are easily affected by external factors. Scholars have made such impressive progress in brain tumors classification by using convolutional neural network (CNN). However, there are still some problems: (i) There are many parameters in CNN, which require much calculation. (ii) The brain tumor data sets are relatively small, which may lead to the overfitting problem in CNN. In this paper, our team proposes a novel model (RBEBT) for the automatic classification of brain tumors. We use fine-tuned ResNet18 to extract the features of brain tumor images. The RBEBT is different from the traditional CNN models in that the randomized neural network (RNN) is selected as the classifier. Meanwhile, our team selects the bat algorithm (BA) to optimize the parameters of RNN. We use fivefold cross-validation to verify the superiority of the RBEBT. The accuracy (ACC), specificity (SPE), precision (PRE), sensitivity (SEN), and F1-score (F1) are 99.00%, 95.00%, 99.00%, 100.00%, and 100.00%. The classification performance of the RBEBT is greater than 95%, which can prove that the RBEBT is an effective model to classify brain tumors.

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“…DenseNet101 comprises 10.2 million trainable parameters and includes one convolutional layer, one max pooling layer, three transition layers, one average pooling layer, one fully connected layer (FCL), and one Softmax layer. It also features four dense block layers, with the third and fourth dense blocks each containing one convolution layer with a stride of 1 × 1 and the third and fourth dense blocks each having a stride of 3 × 3, respectively [ 31 ]. The DenseNet201 model also consists of 10.2 million trainable parameters and consists of one convolution layer, one max pooling layer, three transition layers, one average pooling layer, one fully connected layer, and one softmax layer.…”

Section: Methodsmentioning

confidence: 99%

Multimodal hybrid convolutional neural network based brain tumor grade classification

Rohini,

Praveen,

Mathivanan

et al. 2023

BMC Bioinformatics

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An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result, diagnostic procedures such as computed tomography, magnetic resonance imaging, and positron emission tomography, as well as blood and urine tests, are used to identify brain tumors. However, these methods can be labor-intensive and sometimes yield inaccurate results. Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive equipment, produce more accurate results, and are easy to set up. In this study, we propose a method based on transfer learning, utilizing the pre-trained VGG-19 model. This approach has been enhanced by applying a customized convolutional neural network framework and combining it with pre-processing methods, including normalization and data augmentation. For training and testing, our proposed model used 80% and 20% of the images from the dataset, respectively. Our proposed method achieved remarkable success, with an accuracy rate of 99.43%, a sensitivity of 98.73%, and a specificity of 97.21%. The dataset, sourced from Kaggle for training purposes, consists of 407 images, including 257 depicting brain tumors and 150 without tumors. These models could be utilized to develop clinically useful solutions for identifying brain tumors in CT images based on these outcomes.

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A Hybrid CNN-KNN Model for MRI brain Tumor Classification

Cited by 28 publications

References 9 publications

Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Multimodal hybrid convolutional neural network based brain tumor grade classification

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