Top Research Papers on Brain Tumor Detection

T triangular fuzzy median filtering is applied for image enhancement that helps in accurate segmentation based on unsupervised fuzzy set method and shows better results and less computational time.

The study indicates that the proposed GA edge detection method performs well compared to both classical and fractional-order edge detection methods.

The survey presented here aims to help the researchers to derive the essential characteristics of brain tumor types and identifies various segmentation/classification techniques which are successful for detection of a range of brain diseases.

Two deep learning based approaches for brain tumor detection and classification using the cutting-edge object detection framework YOLO (You Only Look Once) and the deep learning library FastAi, respectively are proposed and can be applied in real-time brain tumors detection for early diagnosis of brain cancer.

This research paper proposed several ways to detect brain cancer and tumors using computational intelligence and statistical image processing techniques and explains the morphology of brain tumors, accessible data sets, augmentation methods, component extraction, and categorization among Deep Learning (DL), Transfer Learning (TL), and Machine Learning (ML) models.

Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually. Nowadays, the advancement of Computer-assisted Diagnosis (CAD), machine learning, and deep learning in specific allow the radiologist to more reliably identify brain tumors. The traditional machine learning methods used to tackle this problem require a handcrafted feature...

The potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis is demonstrated with the highest accuracy of 99.75%, significantly outperforming other existing methods.

A transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors and reveals that using EfficientNetB2 as the underlying framework yields significant performance improvements.

This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis.

This research work provides an efficient method for brain Tumor segmentation based on the Improved Residual Network (ResNet) that achieves competitive performance over the traditional methods like CNN and Fully Convolution Neural Network in more than 10% improved accuracy, recall, and f-measure.

In the proposed work, a self defined Artificial Neural Network (ANN) and Convolution Neural Network is applied in detecting the presence of brain tumor and their performance is analyzed.

This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images and examined the core segmentation algorithms of each research paper in detail.

The findings highlight the role of ctDNA as a noninvasive biomarker and its potential value for personalized risk stratification and treatment guidance in patients with CNSL.

This research work is to present a detailed critical analysis of the research and findings already done to detect and classify brain tumor through MRI images in the recent past, and highlights the merits and demerits of deep neural networks.

This research demonstrated that fine tuning a state-of-the-art YOLOv7 model through transfer learning significantly improved its performance in detecting gliomas, meningioma, and pituitary brain tumors.

Faster R-CNN deep learning algorithm was proposed for detecting the tumor and marking the area of their occurrence with Region Proposal Network (RPN) and results demonstrate that it is able to achieve an average precision of 75.18% for glioma, 89.45% for meningioma and 68.

This work compares the effect of several conventional data augmentation schemes on the ResNet50 network for brain tumor detection and concludes that the proposed method is different from the other conventional methods with a significance level of 0.05 through the Kruskal Wallis test statistic.

A novel correlation learning mechanism (CLM) for deep neural network architectures that combines convolutional neural network (CNN) with classic architecture that helps CNN to find the most adequate filers for pooling and convolution layers.

The proposed work involves the approach of deep neural network and incorporates a CNN based model to classify the MRI as "Tumour DETECTED" or "TUMOUR Not DETECTed" and captures a mean accuracy score of 96.08% with fscore of 97.3.

A deep convolutional neural network (CNN) EfficientNet-B0 base model is fine-tuned with proposed layers to efficiently classify and detect brain tumor images and outperforms other CNN models by achieving the highest classification accuracy, precision, recall, and area under curve values surpassing other state-of-the-art models.