A Comprehensive Review of Brain Tumour Detection Mechanisms

Abstract

The brain is regarded as the central part of the human body and has a very complicated structure. The abnormal growth of tissue inside the brain is called a brain tumour. Tumour detection at an early stage is the most difficult task in the discipline of health. In this review article, the authors have deeply analysed and reviewed the brain tumour detection mechanisms which include manual, semi- and fully automated techniques. Today, fully automated mechanisms apply deep learning (DL) methods for tumour detection in brain magnetic resonance images (MRIs). This paper deals with previously published research articles relevant to various brain tumour detection techniques. Review of various types of tumours, MRI modalities, datasets, filters, segmentation methods and DL techniques like long short-term memory, gated recurrent unit network, convolution neural network, auto encoder, deep belief network, recurrent neural network, generative adverse network and deep stacking networks have been included in this paper. It has been observed from the analysis that the use of DL techniques in the detection of brain tumours improves accuracy. Finally, this paper reveals research gaps, limitations of existing methods, challenges in tumour detection and contributions of the proposed article.

Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach

“…Traditional methods relied on handcrafted features and classical machine learning algorithms [6], paving the way for early endeavors in deep learning for MRI detection. These techniques utilized texture and shape features like gabor filters, gray level co-occurrence matrices (GLCM), zernike moments, region, circularity, and wavelet transformations [7], [8]. Classifiers such as markov random field (MRF), artificial ❒ ISSN: 2088-8708 neural network (ANN), and support vector machine (SVM) achieved accuracy rates ranging from 75% to 98%, playing a vital role in tissue categorization [9].…”

Enhanced Analysis: GAN-Based Medical Imaging Analysis for Brain Tumor Detection

“…The implementation of the Rearranged Pyramid Pooling Module (IPPM) expansion has strengthened the VGG19 strategy. Our capitalizing on the complementary characteristics of VGG19 and IPPM, our research provides a fresh outlook on the classification of BT 6 .…”