Brain Tumor Classification Using MRI Images with K-Nearest Neighbor Method

Ramdlon, Rafi Haidar; Kusumaningtyas, Entin Martiana; Karlita, Tita

doi:10.1109/elecsym.2019.8901560

Cited by 29 publications

(14 citation statements)

References 2 publications

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“…Additionally, the proposed method outperforms Ramdlon [15], as we used a deep learning-based classification method without segmentation or feature extraction stages in this analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, Kumar and Vijay Kumar [14] developed a theory factor analysis method for feature reduction and the radial base function of the SVM kernel base for classification. Ramdlon et al [15] categorized brain tumors into three types: Astrocytoma, Glioblastoma, and Oligodendroglioma. They performed segmentation using the watersheds approach and feature extraction using shape-based features using a KNN classifier.…”

Section: Introductionmentioning

confidence: 99%

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Brain Tumor Classification in MRI Images Using En-CNN

Purnomo¹

2021

IJIES

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Brain tumors are among the most common diseases of the central nervous system and are harmful. Early diagnosis is essential for patient proper treatment. Radiologists need an automated system to identify brain tumor images successfully. The identification process is often a tedious and error-prone task. Furthermore, brain tumor binary classification is often characterized by malignant and benign because it involves multi-sequence MRI (T1, T2, T1CE, and FLAIR), making radiologist's work quite challenging. Recently, several classification methods based on deep learning are being used to classify brain tumors. Each model's performance is highly dependent on the CNN architecture used. Due to the complexity of the existing CNN architecture, hyperparameter tuning becomes a problem in its application. We propose a CNN method called en-CNN to overcome this problem. This method is based on VGG-16 that consists of seven convolutional networks, four ReLU, and four max-pooling. The proposed method is used to facilitate the hyperparameter tuning. We also proposed a new approach in which the classification of brain tumors is done directly without priorly doing the segmentation process. The new approach consists of the following stages: preprocessing, image augmentation, and applying the en-CNN method. Our new approach is also doing the classification using four MRI sequences of T1, T1CE, T2, and FLAIR. The proposed method delivers accuracy on the MRI multi-sequence BraTS 2018 dataset with an accuracy of 95.5% for T1, 95.5% for T1CE, 94% for T2, and 97% for FLAIR with mini-batch size 128 and epoch 200 using ADAM optimizer. The accuracy was 4% higher than previous research in the same dataset.

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“…Additionally, the proposed method outperforms Ramdlon [15], as we used a deep learning-based classification method without segmentation or feature extraction stages in this analysis.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain Tumor Classification in MRI Images Using En-CNN

Purnomo¹

2021

IJIES

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“…Therefore, the algorithm learns through receiving either rewards or penalties for the actions it performs [ 107 ]. Machine learning has been used in the classification of brain tumors from MRI images, and promising classification performance has been reported [ 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 ].…”

Section: Brain Tumor Classification Methodsmentioning

confidence: 99%

“…Several methods have been developed for reducing noises based on statistical property and frequency spectrum distribution [ 119 ]. In addition to denoising, tasks such as removing tags, smoothing the foreground region, intensity inhomogeneity correction, maintaining relevant edges, resizing, cropping, and skull stripping are part of pre-processing [ 110 , 111 , 112 ].…”

Section: Brain Tumor Classification Methodsmentioning

confidence: 99%

A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

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A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

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“…There is a variety of algorithms that have widely emerged in the field of medical imaging as a part of artificial intelligence, while their main goal is to learn inherent patterns of training data using algorithms like Artificial Neural Networks (ANN) [5], K-Nearest Neighbors (KNN) [6] and Support Vector Machine (SVM) [7,8]. However, another category of algorithms named Convolutional Neural Networks (CNN) seem to be the most ideal way of dealing with image or video problems due to higher classification performances.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Detection Based on Features Extracted and Classified Using a Low-Complexity Neural Network

Papageorgiou¹

2021

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Brain tumor detection or brain tumor classification is one of the most challenging problems in modern medicine, where patients suffering from benign or malignant brain tumors are usually characterized by low life expectancy making the necessity of a punctual and accurate diagnosis mandatory. However, even today, this kind of diagnosis is based on manual classification of magnetic resonance imaging (MRI), culminating in inaccurate conclusions especially when they derive from inexperienced doctors. Hence, trusted, automatic classification schemes are essential for the reduction of humans’ death rate due to this major chronic disease. In this article, we propose an automatic classification tool, using a computationally economic convolutional neural network (CNN), for the purposes of a binary problem concerning MRI images depicting the existence or the absence of brain tumors. The proposed model is based on a dataset containing real MRI images of both classes with nearly perfect validation-testing accuracy and low computational complexity, resulting a very fast and reliable training-validation process. During our analysis we compare the diagnostic capacity of three alternative loss functions, validating the appropriateness of cross entropy function, while underlining the capability of an alternative loss function named Jensen-Shannon divergence since our model accomplished nearly excellent testing accuracy, as with cross-entropy. The multiple validation tests applied, enhancing the robustness of the produced results, render this low-complexity CNN structure as an ideal and trustworthy medical aid for the classification of small datasets.

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Brain Tumor Classification Using MRI Images with K-Nearest Neighbor Method

Cited by 29 publications

References 2 publications

Brain Tumor Classification in MRI Images Using En-CNN

Brain Tumor Classification in MRI Images Using En-CNN

A Survey of Brain Tumor Segmentation and Classification Algorithms

Brain Tumor Detection Based on Features Extracted and Classified Using a Low-Complexity Neural Network

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