AUTHOR=Reddy K. Rasool , Batchu Raj Kumar , Polinati Srinivasu , Bavirisetti Durga Prasad 

TITLE=Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1157155

DOI=10.3389/fnhum.2023.1157155

ISSN=1662-5161

ABSTRACT=Brain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body. It is one of the leading causes of the increase in the mortality rate of adults worldwide. So, early prediction of brain tumors is crucial in saving a patient’s life. In addition, selecting a suitable imaging sequence also plays a significant role in treating brain tumors. Among available techniques, the Magnetic Resonance (MR) imaging modality is widely used due to its non-invasive nature and ability to represent the inherent details of the brain tissue. Several computer-assisted diagnoses (CAD) approaches have recently been developed based on these observations. However, there is scope for improvement due to tumor characteristics and image noise variations. Hence, it is essential to establish a new paradigm. Based on this idea, we suggest a medical decision-support system for detecting and differentiating brain tumors from MR images in this work. In the implemented approach, initially, we improve the contrast and brightness using the Tuned single-scale Retinex (TSSR) approach. Then, we extract the infected tumor region(s) using maximum entropy-based thresholding and morphological operations. Further, we obtain the relevant texture features based on the non-local binary pattern (NLBP) feature descriptor. Finally, the extracted features are subjected to Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and GentleBoost (GB). From the analysis of experimental results, it is noted that the presented model achieved 99.75% classification accuracy and 91.88% dice similarity score. Hence, our method can be used as a supportive clinical tool for physicians during the diagnosis of brain tumors.