Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

Abstract

Background Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. Methods This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. Results According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. Conclusions This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Diagnosis of cognitive and motor disorders levels in stroke patients through explainable machine learning based on MRI

“…According to the model, diagnostics Maximum was the feature that contributed most to the prediction. Moreover, the glcm Cluster Prominence was the second most contributing feature, where GLCM is derived from Gray Level Co‐occurrence Matrix, cluster prominence indicates the asymmetry of the pixel pair distribution in the lesion area, 38 which indicated the quantify the distribution of the signal. This metric allows the physician to differentiate between regions of uniform distribution of the protein of interest and its tendency to form patches or aggregates area.…”

Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

“…The genetic algorithm was also employed to optimize the weights and biases of the initial layer of the CNN. The evaluation metric value of the dice coefficient exceeded 0.90 [11], [12], [13].…”

“…HGG, in particular, exhibits a more aggressive nature and is distinguished by its rapid growth. Individuals who receive a diagnosis of high-grade glioma (HGG) generally experience a life expectancy of two years or less [11], [12], [13]. This highlights the significant need for precise and prompt detection to facilitate appropriate treatment and care.…”

Artificial Intelligence-Based Medical Data Mining

“…Vijithananda et al [56] extracted features from MRI ADC images of a brain tumor. The following features were extracted from labeled MRI brain ADC image slices from 195 patients: Skewness, cluster shade, pixel values (he demographics), prominence, Grey Level Co-occurrence Matrix (GLCM) features, energy, contrast, entropy, variance, mean, correlation, homogeneity, and kurtosis.…”