Healthc Inform Res.  2022 Jan;28(1):46-57. 10.4258/hir.2022.28.1.46.

Texture, Morphology, and Statistical Analysis to Differentiate Primary Brain Tumors on Two-Dimensional Magnetic Resonance Imaging Scans Using Artificial Intelligence Techniques

• Bhattacharjee S
• Prakash D
• Kim CH
• Kim HC
• Choi HK

• KMID: 2526493
• DOI: http://doi.org/10.4258/hir.2022.28.1.46

Abstract

Objectives
A primary brain tumor starts to grow from brain cells, and it occurs as a result of errors in the DNA of normal cells. Therefore, this study was carried out to analyze the two-dimensional (2D) texture, morphology, and statistical features of brain tumors and to perform a classification using artificial intelligence (AI) techniques.
Methods
AI techniques can help radiologists to diagnose primary brain tumors without using any invasive measurement techniques. In this paper, we focused on deep learning (DL) and machine learning (ML) techniques for texture, morphological, and statistical feature classification of three tumor types (namely, glioma, meningioma, and pituitary). T1-weighted magnetic resonance imaging (MRI) 2D scans were used for analysis and classification (multiclass and binary). A total of 102 features were calculated for each tumor, and the 20 most significant features were selected using the three-step feature selection method, which included removing duplicate features, Pearson correlations, and recursive feature elimination.
Results
From the predicted results of multiclass and binary classification, a long short-term memory binary classification (glioma vs. meningioma) showed the best performance, with an average accuracy, recall, precision, F1-score, and kappa coefficient of 97.7%, 97.2%, 97.5%, 97.0%, and 94.7%, respectively.
Conclusions
The early diagnosis of primary brain tumors is very important because it can be the key to effective treatment. Therefore, this research presents a method for early diagnoses by effectively classifying three types of primary brain tumors.

Keyword

Brain Tumor; Magnetic Resonance Imaging; Deep Learning; Machine Learning; Classification

Figure

• 

  Figure 1 T1-weighted two-dimensional magnetic resonance imaging scans of brain tumors with three different angles: axial (first row), coronal (second row), and sagittal (third row). (A) Glioma. (C) Meningioma. (E) Pituitary tumor. (B, D, and F) Mask images of brain tumors generated from (A), (C), and (E), respectively.

• 

  Figure 2 Flowchart of the research pipeline for brain tumor classification. 2D MRI: two-dimensional magnetic resonance imaging, GLCM: gray level co-occurrence matrix, GLRLM: gray level run length matrix, GLSZM: gray level size zone matrix, GLDM: gray level dependence matrix, NGTDM: neighboring gray-tone difference matrix, LSTM: long short-term memory, SVM: support vector machine, KNN: k-nearest neighbor, LR: logistic regression, RF: random forest, LDA: linear discriminant analysis.

• 

  Figure 3 Extraction of regions of interest for three different classes of brain tumors. (A–C) Gliomas. (D–F) Meningiomas. (G–I) Pituitary tumors.

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  Figure 4 Process of feature engineering and selection. FOS: first-order statistics, 2D: two-dimensional, GLCM: gray level co-occurrence matrix, GLRLM: gray level run length matrix, GLSZM: gray level size zone matrix, GLDM: gray level dependence matrix, NGTDM: neighboring gray-tone difference matrix.

• 

  Figure 5 Receive operating characteristic (ROC) curve for analyzing the performance of the models and comparing the results of each classifier. (A) ROC curve of multiclass classification (glioma vs. meningioma vs. pituitary). (B) ROC curve of glioma vs. meningioma classification. (C) ROC curve of glioma vs. pituitary classification. (D) ROC curve of meningioma vs. pituitary classification. LSTM: long short-term memory, SVM: support vector machine, KNN: k-nearest neighbor, LR: logistic regression, RF: random forest, AUC: area under the curve.

Reference

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