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

Subrata Bhattacharjee, Deekshitha Prakash, Cho-Hee Kim, Hee-Cheol Kim, Heung-Kook Choi
2022 Healthcare Informatics Research  
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
more » ... 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.
doi:10.4258/hir.2022.28.1.46 pmid:35172090 pmcid:PMC8850171 fatcat:ojhfcomkgbhtrptwfnvbim4rka