Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques

Vijithananda, Sahan M.; Jayatilake, Mohan L.; Hewavithana, Badra; Gonçalves, Teresa; Rato, Luís; Weerakoon, Bimali Sanjeevani; Kalupahana, Tharindu D.; Silva, Anil D.; Dissanayake, Karuna

doi:10.21203/rs.3.rs-1186157/v1

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…When tested on a large MRI dataset, LeaSE outperformed traditional models like ResNet101 and did so more efficiently. This innovative approach could revolutionize brain tumour diagnosis using MRIs [23].…”

Section: Brain Tumour Classification Using Machine Learning and Deep ...mentioning

confidence: 99%

MEHW‐SVM multi‐kernel approach for improved brain tumour classification

Dheepak,

Anita Christaline,

Vaishali

2023

IET Image Processing

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The human brain, the primary constituent of the nervous system, exhibits distinctive complexities that present considerable difficulties for healthcare practitioners, specifically in categorizing brain tumours. Magnetic resonance imaging is a widely favoured imaging modality for detecting brain tumours due to its extensive range of image characteristics and utilization of non‐ionizing radiation. The primary objective of the current investigation is to differentiate between three distinct classifications of brain tumours by introducing a novel methodology. The utilization of a combined feature extraction technique that integrates novel global grey level co‐occurrence matrix and local binary patterns is employed, thereby offering a comprehensive representation of the structural and textural information contained within the images. Principal component analysis is used to improve the model's efficiency for effective feature selection and dimensionality reduction. This study presents a novel framework incorporating four separate kernel functions, Minkowski–Gaussian, exponential support vector machine (SVM), histogram intersection SVM, and wavelet kernel, into a SVM classifier. The ensemble kernel employed in this study is specifically designed to classify glioma, meningioma, and pituitary tumours. Its implementation enhances the model's robustness and adaptability, surpassing the performance of conventional single‐kernel SVM approaches. This study substantially contributes to medical image classification by utilizing innovative kernel functions and advanced machine‐learning techniques. The findings demonstrate the potential for enhanced diagnostic accuracy in brain tumour cases. The presented approach shows promise in effectively addressing the intricate challenges associated with classifying brain tumours.

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Section: Brain Tumour Classification Using Machine Learning and Deep ...mentioning

confidence: 99%

MEHW‐SVM multi‐kernel approach for improved brain tumour classification

Dheepak,

Anita Christaline,

Vaishali

2023

IET Image Processing

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“…The process iterates until the algorithm achieves the highest prediction accuracy and the developed model is able to address the intended problem with high accuracy level (See Figure 1). The accuracy level of the developed ML model is optimized by tuning the hyper-parameters of the model [35]. Among the various types of supervised learning algorithms, Neural Networks, Naïve Bayes, Linear Regression, Logistic Regression, Support Vector Machines, K-Nearest Neighbor, Decision Tree, and Random Forest algorithms are the most commonly used ones.…”

Section: Machine Learningmentioning

confidence: 99%

Texture Feature Analysis of MRI-ADC Images to Differentiate Glioma Grades Using Machine Learning Techniques.

Vijithananda

Jayatilake

Gonçalves

et al. 2022

Preprint

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Background: Apparent Diffusion Coefficient (ADC) of Magnetic Resonance Imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using Diffusion-weighted imaging (DWI). This study focuses on developing a robust Machine Learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and Grey Level Co-occurrence Matrix (GLCM) texture features of ADC. Methods: A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. Results: According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772±0.0237) performed the highest score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. Conclusion: The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

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“…Higher order cumulant features were used since they are resistant to Gaussian noise in the original data. In remote sensing applications like fire monitoring and detection, higher-order statistical features are rarely used (Vijithananda, Jayatilake et al 2022). The present study evaluates higher order cumulants (order 3) that extract the cumulant coefficients from images using an unbiased approach.…”

Section: Introductionmentioning

confidence: 99%

Fire images classification using high order statistical features

Harkat¹,

Nascimento²,

Bernardino³

et al. 2022

Advances in Forest Fire Research 2022

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Wildfires and forest fires have devastated millions of hectares of forest across the world over the years. Computer vision-based fire classification, which classifies fire pixels from non-fire pixels in image or video datasets, has gained popularity as a result of recent innovations. A conventional machine learning-based approach or a deep learning-based approach can be used to distinguish fire pixels from an image or video. Deep learning is currently the most prominent method for detecting forest fires. Although deep learning algorithms can handle large volumes of data, typically ignore the differences in complexity among training samples, limiting the performance of training models. Moreover, in real-world fire scenarios, deep learning techniques with little data and features underperform. The present study utilizes a machine learning-based approach for extracting features of higher-order statistical methods from pre-processed images from publicly available datasets: Corsican and FLAME, and a private dataset: Firefront Gestosa. It should be noted that handling multidimensional data to train a classifier in machine learning applications is complex. This issue is addressed through feature selection, which eliminates duplicate or irrelevant data that has an effect on the model's performance. A greedy feature selection criterion is adopted in this study to select the most significant features for classification while reducing computational costs. The Support Vector Machine (SVM) is a conventional machine classifier that works on discriminative features input obtained using the MIFS, feature selection technique. The SVM uses a Radial Basis Function (RBF) kernel to classify fire and non-fire pixels, and the model's performance is assessed using assessment metrics like overall accuracy, sensitivity, specificity, precision, recall, F-measure, and G-mean.

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Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques

Cited by 6 publications

References 29 publications

MEHW‐SVM multi‐kernel approach for improved brain tumour classification

MEHW‐SVM multi‐kernel approach for improved brain tumour classification

Texture Feature Analysis of MRI-ADC Images to Differentiate Glioma Grades Using Machine Learning Techniques.

Fire images classification using high order statistical features

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