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.

This study conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions.

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.

The Caps-VGGNet hybrid model is proposed, which integrates the CapsNet model with the VGGNet model by adding the layers of VGG net to address the challenge of requiring large datasets by automatically extracting and classifying features.

Several transfer learning based deep learning methods are analyzed using number of traditional classifiers to detect the brain tumor using 2D Magnetic Resonance images and it is shown that the best model achieved an accuracy of 99.39% with a 10-fold cross validation.

This study aims to classify brain tumors such as glioma, meningioma, and pituitary tumor from brain MR images by using CNN architecture and CNN-based transfer learning models.

The various molecular mediators that regulate GBM angiogenesis are highlighted and summarized with focus on recent clinical research on the potential of exploiting angiogenic pathways as a strategy in the treatment of GBM patients.

A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the qua...

The proposed hybrid model provided more effective and improvement techniques for classification and with threshold-based segmentation in terms of detection and the overall accuracy of the hybrid CNN-SVM is obtained.

Analysis of the functional connectome using an individually applicable resting-state fMRI marker revealed that abnormalities of functional connectivity could be detected not only adjacent to the visible lesion but also in distant brain tissue, even in the contralesional hemisphere, associated with tumor biology and cognitive function.

Many molecules have been developed in the last years with a better penetration across BBB, resulting in better progression-free survival and overall survival compared to older molecules, and promising studies concerning neural stem cells, CAR-T (chimeric antigen receptors) strategies and immunotherapy with checkpoint inhibitors are ongoing.

This paper investigates the performance of several deep learning architectures including Visual Geometry Group 16 (VGG16), InceptionV3, VGG19, ResNet50, InceptionResNetV2, Xception, and IVX16 and proposes a transfer learning(TL) based multiclass classification model called IVX 16 based on the three best-performing TL models.

The present review aimed to discuss contemporary scientific literature involving differences between the tumor microenvironment (TME) in melanoma, lung cancer, and breast cancer in their primary site and TME in brain metastases (BM).

Different traditional and hybrid ML models were built and analyzed in detail to classify the brain tumor images without any human intervention to identify the best transfer learning model to classify brain tumors based on neural networks which outperforms all the other developed models.

A novel architecture for Brain tumor classification and tumor type object detection using the RCNN technique is proposed which has been analyzed using two publicly available datasets from Figshare and Kaggle.

Experimental results prove the robustness of the proposed architecture which has increased the detection accuracy of a variety of brain diseases in a short time.

The findings suggest the biomimetic nanotechnology provides a new insight for the design of BBB‐crossing nanomaterials and is promising to treat brain diseases.

Current clinical applications of HT in neuro-oncology and ongoing preclinical research aiming to advance HT approaches to clinical practice are reviewed and magnetic hyperthermia therapy (MHT), which relies on the use of magnetic nanoparticles and alternating magnetic fields, is a new quite promising HT treatment approach for brain tumors.

Although no risk factor accounting for a large proportion of brain and other CNS tumors has been discovered, the use of high throughput “omics” approaches and improved detection/measurement of environmental exposures will help refine the current understanding of these factors and discover novel risk factors for this disease.

Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency.

This paper proposes an optimization-driven technique, namely Whale Harris Hawks optimization (WHHO) for brain tumor detection using MR images, which outperformed other methods with maximal accuracy, maximal specificity, and maximal sensitivity.

The proposed method for classifying brain tumors using MRI is superior to the existing literature, indicating that it can be used to quickly and accurately classify brain tumors.