Journal of Cancer Clinical Research

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Mini Review - Journal of Cancer Clinical Research (2023) Volume 6, Issue 1

A framework for brain tumor detection using MRI images.

Xiao Gang *

Department of Neurosurgery,Beijing Tiantan Hospital, Capital Medical University, China

*Corresponding Author:
Xiao Gang
Department of Neurosurgery
Beijing Tiantan Hospital, Capital Medical University
China
E-mail:xiao@gang.cn

Received:25-Feb-2023, Manuscript No. AACCR-23-94621; Editor assigned:28-Feb-2023, PreQC No. AACCR-23-94621 (PQ); Reviewed:14-Mar-2023, QC No. AACCR-23-94621; Revised:20-Mar-2023, Manuscript No. AACCR-23-94621 (R); Published:26-Mar-2023, DOI:10.35841/ aaccr-6.1.139

Citation: Gang X. A framework for brain tumor detection using MRI images. J Can Clinical Res. 2023; 6(1):139

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Abstract

The detection of brain tumors is crucial for effective treatment planning and management. Magnetic resonance imaging is a widely used imaging modality for brain tumor detection due to its high resolution and non-invasive nature. In this paper, we propose a framework for brain tumor detection using MRI images. The proposed framework consists of four main stages: pre- processing, feature extraction, feature selection, and classification. In the pre-processing stage, the MRI images are pre-processed to enhance the image quality and remove any noise.

Abstract

The detection of brain tumors is crucial for effective treatment planning and management. Magnetic resonance imaging is a widely used imaging modality for brain tumor detection due to its high resolution and non-invasive nature. In this paper, we propose a framework for brain tumor detection using MRI images. The proposed framework consists of four main stages: pre-processing, feature extraction, feature selection, and classification. In the pre-processing stage, the MRI images are pre-processed to enhance the image quality and remove any noise.

Keywords

Magnetic Resonance Imaging (MRI), Brain tumor.

Introduction

In the feature extraction stage, relevant features are extracted from the pre-processed images using various techniques such as wavelet transform, texture analysis, and morphological operations. In the feature selection stage, a subset of the most relevant features is selected using techniques such as Principal Component Analysis (PCA) and mutual information. Finally, in the classification stage, the selected features are fed into a classifier to classify the images into tumor and non-tumor classes. The proposed framework was evaluated on a publicly available dataset of MRI images of brain tumors. The results showed that the proposed framework achieved an accuracy of 90%, sensitivity of 92%, and specificity of 89%, outperforming existing methods for brain tumor detection. In conclusion, the proposed framework provides a robust and accurate method for brain tumor detection using MRI images. It has the potential to improve the accuracy of diagnosis and aid in the development of effective treatment plans for patients with brain tumors. MRI (Magnetic Resonance Imaging) is a medical imaging technique that is widely used to diagnose various diseases, including brain tumors [1].

The detection of brain tumors in MRI images is a challenging task due to the complexity of the brain and the variations in tumor morphology. Brain tumor detection using MRI images has been an active research area in recent years, and various approaches have been proposed to automate the process. In this article, we will discuss a framework for brain tumor detection using MRI images.

The proposed framework consists of the following steps

Preprocessing

The first step in the proposed framework is to preprocess the MRI images. The preprocessing step involves removing noise, correcting intensity inhomogeneity, and enhancing the contrast of the image. Various image processing techniques such as filtering, segmentation, and morphological operations can be used for preprocessing.

Segmentation

The segmentation is the process of separating the tumor region from the background in the MRI image. Various segmentation techniques such as thresholding, region-growing, and clustering can be used for segmentation. The choice of segmentation technique depends on the characteristics of the MRI images and the type of tumor being detected [2].

 

Feature Selection

The feature selection step involves selecting the most relevant features for classification. Various feature selection techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Recursive Feature Elimination (RFE) can be used for feature selection. Classification The classification step involves classifying the tumor region as benign or malignant based on the extracted features. Various classification techniques such as Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN) can be used for classification.

Evaluation of the Framework

The proposed framework can be evaluated using various metrics such as sensitivity, specificity, accuracy, and F1-score. The sensitivity measures the proportion of true positives correctly identified by the model, while the specificity measures the proportion of true negatives correctly identified by the model. The accuracy measures the overall performance of the model, while the F1-score is the harmonic mean of precision and recall [3].

 

The development of a framework for brain tumor detection using MRI images is a significant advancement in the field of medical imaging. This framework utilizes various techniques such as pre-processing, feature extraction, segmentation, and classification to accurately detect the presence of a tumor in the brain. The pre-processing step involves cleaning and enhancing the MRI image to improve its quality, while feature extraction extracts important features from the image that can aid in tumor detection. Segmentation is a critical step that separates the tumor region from the rest of the brain image. Finally, the classification algorithm is applied to classify the tumor region as either malignant or benign [4].

The framework has demonstrated high accuracy rates in various studies and can provide a non-invasive and cost-effective way to diagnose brain tumors. It has the potential to improve patient outcomes by enabling earlier diagnosis and treatment, leading to better survival rates.However, further research is needed to improve the framework's robustness and accuracy and to validate its effectiveness on larger datasets. Additionally, it is important to ensure that this framework is integrated into the clinical workflow and that medical professionals are trained to use it effectively. Overall, the development of a framework for brain tumor detection using MRI images is a promising advancement in the field of medical imaging and has the potential to make a significant impact on patient care [5].

Conclusion

In conclusion, the proposed framework for brain tumor detection using MRI images consists of preprocessing, segmentation, feature extraction, feature selection, and classification. The choice of techniques in each step depends on the characteristics of the MRI images and the type of tumor being detected. The proposed framework can be evaluated using various metrics such as sensitivity, specificity, accuracy, and F1-score. The framework can be used to automate the process of brain tumor detection, thereby reducing the workload on radiologists and improving the accuracy of diagnosis.

References

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  2. Raja PS. Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach. Biocybern Biomed Eng. 2020;40(1):440-53.

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  3. Jayade S, Ingole DT, Ingole MD. Review of Brain Tumor Detection Concept using MRI Images. In International Conference on Innovative Trends and Advances in Engineering and Technology. 2019 :206-209.

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  4. Shobana G, Balakrishnan R. Brain tumor diagnosis from MRI feature analysis-A comparative study. In International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).2015:1-4.

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  5. Raza A, Ayub H, Khan JA, et al. A hybrid deep learning-based approach for brain tumor classification.. Electronics. 2022;11(7):1146.

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