A Literature Review on Brain Tumor Detection and Segmentation

Authors: Shivam Tamrakar, Prof. Mahesh Prasad Parsai

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Abstract

Brain tumor is a major health concern and its early detection is crucial for effective treatment. Magnetic Resonance Imaging (MRI) is a widely used method for detecting brain tumors, but it can be time-consuming and expensive. To overcome these limitations, researchers have proposed deep learning algorithms for the detection and segmentation of brain tumors from MRI images. This literature review provides an extensive and exhaustive guide to the sub-field of brain tumor detection and segmentation. The latest research work done in this domain is summarized and compared to provide insights into the most efficient and effective methods for detecting and segmenting brain tumors. The review focuses on various deep learning algorithms and techniques, including Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs). The review also discusses the challenges associated with brain tumor detection and segmentation, including class imbalance, noise, and variability in tumor size and shape. Additionally, the review highlights the importance of accurate segmentation in brain tumor diagnosis and treatment planning. Overall, this literature review provides valuable insights into the state-of-the-art techniques for brain tumor detection and segmentation from MRI images, and can guide future research in this domain.

Introduction

TThe rise in the number of people who drive electric Brain tumors are one of the most significant health concerns worldwide. They can be cancerous or non-cancerous and can cause severe damage to the brain if not detected and treated early. Early detection of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is commonly used by specialists and neurosurgeons for the detection and diagnosis of brain tumors.[1] However, manual detection and segmentation of brain tumors from MRI images are time-consuming, subjective, and depend on the expertise of the medical professionals involved. To overcome these limitations, researchers have proposed various automated techniques for the detection and segmentation of brain tumors from MRI images.[2] In recent years, deep learning algorithms have shown great promise in the detection and segmentation of brain tumors from MRI images. [3] Deep learning algorithms can analyze a large amount of data and learn complex features to accurately detect and segment brain tumors from MRI images. Various deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs) have been proposed for the detection and segmentation of brain tumors from MRI images.[4] This literature review aims to provide an extensive and exhaustive guide to the sub-field of Brain Tumor Detection and Segmentation. It will focus on the latest research work done in this domain and summarize and compare the proposed methods and techniques for the detection and segmentation of brain tumors from MRI images.[5] The review will discuss the advantages and limitations of different deep learning algorithms and their applications for the detection and segmentation of brain tumors. It will also highlight the challenges and opportunities in this field and suggest future directions for research.

Conclusion

The paper discusses the use of various machine learning techniques, including fuzzy K-means clustering, random forests, and CNN architectures, for brain tumor detection and segmentation. The study compares and highlights some of the key points of state-of-the-art approaches used in this domain.The use of MKSVM algorithm by S. Krishnakumar et al. achieved the highest accuracy of 99.7% on the MMRI dataset. Similarly, a combination of feature extraction algorithm and CNN resulted in a high classification accuracy of 99.12%.One of the challenges faced by researchers in this domain is the lack of large publicly available datasets for training deep learning models. Furthermore, there is a need for structured labeling reports from experts such as neurologists and radiologists to aid future research.Class imbalance is another common issue encountered in brain tumor detection and segmentation. Researchers use data augmentation techniques such as rotating or scaling down existing images to address this issue. However, the anatomical location of the tumor region is often not known to the network, which can be a limitation. Future research in this domain could focus on incorporating information about the tumor region\'s anatomical location in the neural network. However, the high resolution and large size of brain tumor images make training on such images challeng