ICM-BTD: improved classification model for brain tumor diagnosis using discrete wavelet transform-based feature extraction and SVM classifier

Gokulalakshmi, A; Karthik, S.; Karthikeyan, N.; Kavitha, M.

doi:10.1007/s00500-020-05096-z

Cited by 20 publications

(6 citation statements)

References 21 publications

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“…Correlation is an image feature that can be described as a spatial dependence between one pixel and another. The value of the correlation feature is at a distance of [-1, 1] and is obtained from the (3) where 𝑀 is the mean of the image and 𝜎 is the standard deviation of the image as in (4). Energy can be defined as the number of occurrences of the same pixel pair, and energy is obtained from (5).…”

Section: Gray Level Co-occurrence Matrixmentioning

confidence: 99%

“…For research on segmentation, it focuses on how a method can extract the area of the tumor accurately and precisely, while research on the classification of brain tumors focuses on grouping tumor types, whether the tumor contained in the medical image is a benign tumor or a malignant tumor. Research on brain tumors has been proposed and the object used as a research object is magnetic resonance imaging (MRI) images [3] this was chosen because MRI images provide clearer information compared to computerized tomography (CT) images which are visually more likely to be damaged by noise [4]. The difference between the two tumor types is that low-grade glioma (LGG) tends not to actively spread to the surrounding area, and so is the reverse for the high-grade glioma TELKOMNIKA Telecommun Comput El Control  Support vector machine based discrete wavelet transform for … (Ajib Susanto) 593 (HGG) type [5].…”

Section: Introductionmentioning

confidence: 99%

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Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

et al. 2023

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Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image. Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

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Section: Gray Level Co-occurrence Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

et al. 2023

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“…However, this approach has higher false positives which reduce the effectiveness of classification. Gokulalakshmi et al [11] [12] developed a hybrid grey wolf optimizer-artificial neural network (HGWO-ANN) classification approach to tumor detection. This approach classified the tumor types with an accuracy of 94.45 % and FPR of 8.5 % but it incurs high computation complexity.…”

Section: Related Workmentioning

confidence: 99%

MRI Brain Tumor Detection Using Boosted Crossbred Random Forests and Chimp Optimization Algorithm Based Convolutional Neural Networks

2022

IJIES

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Early detection of the brain tumors can help in providing sufficient treatment to completely cure the disease. Machine Learning (ML) and Deep Learning (DL) algorithms are utilized predominantly in recent years for the tumor detection process from the brain Magnetic Resonance Imaging (MRI). In this paper, an effective tumor detection framework is proposed by utilizing a hybrid segmentation algorithm and a new hyper-parameter optimized Convolutional Neural Networks (CNN) based classifier using Chimp Optimization Algorithm (COA). The framework includes pre-processing, segmentation, feature extraction and classification stages. The pre-processing stage performs the denoising using wavelet filter and image enhancement using adaptive histogram equalization. Then the Boosted Crossbred Random Forests (BCRF) is used to segment the regions of interest to identify skull, tumor and lesions. The features are extracted using the Grey-Level Co-occurrence Matrix (GLCM) and Gabor filter. Finally, the tumor classification is achieved using the COA-CNN. The COA-CNN is developed by utilizing the COA to tune the hyper-parameters of CNN to obtain optimal CNN architecture. Experimental evaluation was performed on a dataset of 3264 MRI brain images containing normal and tumor images. No tumor, Glioma, Meningioma, and Pituitary tumor are classes of the MRI images which are correctly classified by the proposed BCRF and COA-CNN based detection model with a testing accuracy of 95.18% and reduced processing time. The results indicate the efficacy of the proposed method in classifying brain tumors for assisting physicians in early tumor diagnosis and treatments.

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“…Study [6] used an online digital library of MRI images of the brain to train "Machine Learning" for feature selection, and apply the "Support Vector Machine (SVM)" classifier to identify the kind of tumor present in new images. The research [7] proposed a model that we name the enhanced classification model for brain tumor diagnosis. The method is used to estimate the correct categorization of tumor pictures based on input MRI images.…”

Section: IImentioning

confidence: 99%

Brain Tumor Image Processing Using Fine-Tuned Resnet-101 Classification Model

Gupta

2022

ijfrcsce

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Medical image processing relies heavily on the diagnosis of brain tumor images. It aids doctors in determining the correct diagnosis and management. One of the primary imaging methods for studying brain tissue is MR imaging. In recent years, deep learning techniques have shown significant potential in image processing. However, the modest quantity of medical images is a restriction of the classification of medical images. As a result of this restriction, fewer medical photos are available. Fine-tuned ResNet-101 (FR-101) is proposed to classify the brain tumor images to counteract this issue. Weiner filter is used to de-noise the acquired raw MR images, and the adaptive histogram equalization technique is used to improve contrast. A stacked autoencoder is utilized in the segmentation procedure to separate the tumor from healthy brain parts from the preprocessed data. The marker-based watershed technique is used to identify the tumor location and structure in the segmented data. The recommended approach is then used in the classification stage. To obtain the highest level of accuracy for our research, accuracy, precision, f1-score, recall, and mean absolute error are the measures of success are studied as well as a comparison of the suggested approach with a few other existing methods.

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ICM-BTD: improved classification model for brain tumor diagnosis using discrete wavelet transform-based feature extraction and SVM classifier

Cited by 20 publications

References 21 publications

Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

MRI Brain Tumor Detection Using Boosted Crossbred Random Forests and Chimp Optimization Algorithm Based Convolutional Neural Networks

Brain Tumor Image Processing Using Fine-Tuned Resnet-101 Classification Model

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