Remu-Net: Multi-Branch Net Framework for 3D Brain Tumor Segmentation

Among the list of dreadful diseases, 'Brain Tumor and Brain Cancer' is one of them. According to survival rate, after the detection of cancerous Brain or Central Nervous System (CNS) tumor, 34% of men and 36% of women survive at least 5 years. Therefore, 'Early Detection of tumor' can be a savior for so many lives and artificial intelligence will make the way very accurate and appropriate. A new non-invasive approach to detect brain tumors in early-stage, cost-efficiently with the lowest risk factor is-'Liquid Biopsy'. It is a technique for analyzing non-solid biological tissue, i.e. - blood, plasma, cerebrospinal fluid (CSF). As human inspection maybe error full and inaccurate, in this paper, we have used 'k-means clustering' in 'Image Processing' as visual perception of artificial intelligence for detection of 'CTC' and we have used the technique 'Convolutional Neural Network' (CNN) for ctDNA detection and 'Support Vector Machine'(SVM) algorithm for classification of ctDNA and normal cell-free DNA (cfDNA). 'ctDNA' could provide clues for growth factor of cancerous cells, type of tumors, location, etc. Therefore, our work provides a new way to early detection and a new prospect for early cure.

The accuracy of three-dimensional (3D) brain tumor image segmentation is of great significance to brain tumor diagnosis. To enhance the accuracy of segmentation, this study proposes an algorithm integrating a cascaded anisotropic fully convolutional neural network (FCNN) and the hybrid level set method. The algorithm first performs bias field correction and gray value normalization on T1, T1C, T2, and fluid-attenuated inversion recovery magnetic resonance imaging (MRI) images for preprocessing. It then uses a cascading mechanism to perform preliminary segmentation of whole tumors, tumor cores, and enhancing tumors by an anisotropic FCNN based on the relationships among the locations of the three types of tumor structures. This simplifies multiclass brain tumor image segmentation problems into three binary classification problems. At the same time, the anisotropic FCNN adopts dense connections and multiscale feature merging to further enhance performance. Model training is respectively conducted on the axial, coronal, and sagittal planes, and the segmentation results from the three different orthogonal views are combined. Finally, the hybrid level set method is adopted to refine the brain tumor boundaries in the preliminary segmentation results, thereby completing fine segmentation. The results indicate that the proposed algorithm can achieve 3D MRI brain tumor image segmentation of high accuracy and stability. Comparison of the whole-tumor, tumor-core, and enhancing-tumor segmentation results with the gold standards produced Dice similarity coefficients (Dice) of 0.9113, 0.8581, and 0.7976, respectively.

Background: Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival. Introduction: Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancement. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the growth. Methods: This article presents a comprehensive evaluation of conventional machine learning models as well as evolving deep learning techniques for brain tumor segmentation and classification. Conclusion: In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping requires redundancy correction, the input data training needs to be more exhaustive and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection.

Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.

The need for early detection and treatment makes brain tumour possibly the most critical illness for which radiologists and neuro subject-matter professionals must be skilled. In recent years, computational intelligence (CI) and machine learning (ML) methods have evolved into effective smart instruments for the early detection of brain tumors. Computer-aided diagnosis (CAD) methods primarily rely on various CI approaches in all phases of brain image analysis to build a smart consulting platform that can support radiologists in the detection and identification of brain tumors by offering a second option. Detection and classification of a brain tumour require time and are mainly reliant on the radiologist's skills and expertise. As the patient population has expanded, so has the quantity of data that should be processed, making previous techniques both expensive and inefficient. Deep Learning (DL) methods have lately gained popularity in the field of CI for creating automated methods that can quickly and effectively detect and classify brain tumors. This study suggested a CNN-based brain cancer classification model (BCM-CNN) since DL makes it possible to use a pre-trained CNN framework for medical images, particularly for classifying brain tumors. After hyperparameter adjustment, an Inception-ResnetV2 training framework is developed. To enhance brain tumor detection, the approach uses widely employed pre-trained methods (Inception-ResnetV2), and its outcome is a binary 0 or 1 (Normal-0, Tumor-1). The empirical outcomes demonstrate that the BCM-CNN classifier outperformed the existing methods since the performance of CNN is enhanced through the CNN optimization's hyperparameters. Using the Task 1 dataset of BRaTS 2021, the BCM-CNN obtained 99.98% accuracy.

Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm

Brain tumors are abnormal tissue growth characterized by uncontrolled and rapid cell proliferation. Early detection of brain tumors is critical for improving patient outcomes, and magnetic resonance imaging (MRI) has become the most widely used modality for diagnosis due to its superior image quality and non-invasive nature. Deep learning, a subset of artificial intelligence, has revolutionized automated medical image analysis by enabling highly accurate and efficient classification tasks. The objective of this study is to develop a robust and effective brain tumor detection system using MRI images through transfer learning. A diagnostic framework is constructed based on convolutional neural networks (CNN), integrating both a custom sequential CNN model and pretrained architectures, namely VGG16 and EfficientNetB4, trained on the ImageNet dataset. Prior to model training, image preprocessing techniques are applied to enhance feature extraction and overall model performance. This research addresses the common challenge of limited MRI datasets by combining EfficientNetB4 with targeted preprocessing, data augmentation, and an appropriate optimizer selection strategy. The proposed methodology significantly reduces overfitting, improves classification accuracy on small datasets, and remains computationally efficient. Unlike previous studies that focus solely on CNN or VGG16 architectures, this work systematically compares multiple transfer learning models and demonstrates the superiority of EfficientNetB4. Experimental results on the Br35H dataset show that EfficientNetB4, combined with the ADAM optimizer, achieves outstanding performance with an accuracy of 99.66%, precision of 99.68%, and an F1-score of 100%. The findings confirm that integrating EfficientNetB4 with dataset-specific preprocessing and transfer learning provides a highly accurate and cost-effective solution for brain tumor classification, facilitating rapid and reliable medical diagnosis.

Early detection of brain tumors improves treatment options and increases the patient’s survival percentage. Tumor cells are exceedingly difficult to detect due to their diversity. Manual brain tumors detection for cancer diagnosis from a large number of MRI images obtained in clinical practice is a challenging and time-consuming task. The primary goal of this study is to investigate the ability of various pre-trained DCNN models using transfer learning to detect diseased brain images. This work focuses on constructing an effective and reliable method for detecting brain tumors using MRI to aid neurologists in making quick and appropriate decisions. The framework, which includes a dataset of 3064 images has been created and applied for the identification of brain tumors employing deep learning techniques. We have used conceptual model with Transfer Learning (TL) to verify it and got 98.28% accuracy, 97.51% recall and 97.43% precision. The proposed TL based brain tumor detection algorithm outperforms existing pre-trained models.

Early Detection of Brain Tumor from MRI Images Using Different Machine Learning Techniques

There are no good blood and serum biomarkers for detection, follow up, or prognosis of brain tumors. However, they are needed for more detailed tumor classification, better prognosis estimation and selection of an efficient therapeutic strategy. The aim of this study was to use the epigenetic changes in DNA of peripheral blood samples as a molecular marker to diagnose brain tumors as well as other diseases. We have applied a very precise thin-layer chromatography (TLC) analysis of the global amount of 5-methylcytosine (m5C) in DNA from brain tumors, colon and breast cancer tissues and peripheral blood samples of the same patients. The m5C level in tissue DNA from different brain tumor types, expressed as R coefficient, changes within the range of 0.2–1.6 and overlaps with R of that of blood samples. It negatively correlates with the WHO malignancy grade. The global DNA hypomethylation quantitative measure in blood, demonstrates a big potential for development of non-invasive applications for detection of a low and a high grade brain tumors. We have also used this approach to analyze patients with breast and colon cancers. In all these cases the m5C amount in DNA cancer tissue match with data of blood. This study is the first to demonstrate the potential role of global m5C content in blood DNA for early detection of brain tumors and others diseases. So, genomic DNA hypomethylation is a promising marker for prognosis of various neoplasms as well as other pathologies.

INTRODUCTION Blood biomarkers could enable cost-effective, early detection of brain tumors. While circulating miRNAs are promising candidates, disease specificity is poorly understood. Here, we use an unbiased approach to identify miRNAs which distinguish brain tumor cases from controls, correlate findings with glioblastoma stem cell (GSC) miRNA expression, and characterize the effects of inhibiting select miRNAs on GSC phenotypes. METHODS Patients undergoing surgery at a single center for newly diagnosed brain tumors (pathology-confirmed glioblastoma n=21, meningioma n=27, or brain metastasis n=9) and age-matched controls undergoing elective surgery for degenerative spinal disease (DSD n=24, e.g. microdiscectomy) were recruited for preoperative research blood collection (2019–2024). 798 miRNAs were quantified per serum sample using nCounter (Nanostring). AutoML (JADBio) was applied to identify miRNAs which optimally classify samples by these diagnoses, and model stability analyses confirmed those that classify samples most consistently. Model performance is reported as area under receiver operating characteristic (AUROC) and mean precision (MP). The same miRNAs were quantified in GSC media. Those which distinguished glioblastoma cases from controls were inhibited in vitro via lipid transfection (qPCR-confirmed) to observe effects on GSC growth (Incucyte), viability (CellTiter-Glo®), and self-renewal (sphere formation). RESULTS miRNA-based classification of brain tumor (n=57) versus DSD controls (n=24) yielded excellent performance (AUROC=0.93 [0.88–0.98], MP=0.95 [0.91–0.98]), as did glioblastoma versus DSD (AUROC=0.94 [0.86–1.00], MP=0.95 [0.91–1.00]) and glioblastoma/metastasis versus DSD (AUROC=0.92 [0.85–0.97], MP=0.95 [0.91–0.98]). Some performance-driving miRNAs (miR-150-5p, miR-1283) were under-expressed in glioblastoma cases while others (miR-873-3p, let-7b-5p) were over-expressed. These miRNAs were also expressed in GSC media. Inhibiting let-7b-5p was noted to reduce GSC growth, viability, and self-renewal, while inhibiting others augmented these phenotypes. CONCLUSIONS A preoperative miRNA signature may accurately distinguish brain tumor patients from age-matched controls. Inhibition of these miRNAs affected GSC phenotypes. Future studies will validate classification models in an independent cohort and investigate the effect of miRNA inhibition on GSC transcript expression (RNA-sequencing).

Brain tumors, whether benign or malignant, present a complex and multifaceted challenge in healthcare, affecting individuals across various age groups. Predicting the vulnerability of brain tumors using health risk factors and symptoms is crucial, yet there have been limited research studies, particularly those integrating artificial intelligence (AI) technology. This research explores machine learning models such as support vector machines (SVMs), multi-layer perceptrons (MLPs), and logistic regression (LR) for the early detection of brain tumors. Evaluation metrics, including accuracy, precision, recall, and F1-score, are employed to assess model performance. The results indicate that the SVM outperforms other models, providing a robust foundation for predictive accuracy. To enhance accessibility and usability, the research also integrates these models into a mobile application predictor. The application is beneficial for assisting individuals in early detection by identifying potential risk factors and symptoms that may lead to a brain tumor. In conclusion, the integration of machine learning through a mobile application represents a transformative approach to personalized healthcare. By empowering individuals with cutting-edge technology, this research strives to enhance early detection and decision-making regarding potential brain tumor risks and symptoms, ultimately contributing to improved patient outcomes and quality of life.

Early detection of brain tumors is essential for successful treatment and better patient outcomes. Traditional imaging methods like X-rays, MRIs, CT scans, and PET scans have been important in detecting brain tumors, but they are expensive with many drawbacks and areas where access is limited. Antenna-based method has recently emerged as a practical alternative for real-time, non-invasive detection of brain tumors. This paper explores different antennas and various types of substrates that are adaptable to human sensitive tissues for detecting brain tumors. This review highlights the antenna working principles, and the advantages and challenges associated with each type. The effectiveness of several antenna-based methods in medical diagnostics, including microwave imaging and ultra-wideband (UWB) systems, is discussed. To assess their impact on detection accuracy, essential factors such as penetration depth, resolution, operating frequency, and antenna design are considered. The integration of antennas with machine learning and signal processing techniques is investigated.

Brain tumors are one of the most important causes of the increase in mortality worldwide. Early detection of brain tumors can save many lives. Therefore, brain tumors need to be detected quickly and accurately. Many algorithms have been developed using conventional image processing methods for brain tumor detection. The object detectors developed in all these algorithms consist of several stages in which the success of the current step depends on the success of the previous step. On the other hand, single-stage and two-stage deep learning-based detectors developed in recent years enable faster and more accurate object detection. However, in many deep learning-based detections, it is generally studied to determine the object locations in natural images. In this study, Yolo v2 model which is a singlestage deep learning-based detector is performed for brain tumor tissue detection. Initial results obtained by running the designed detector architecture on different data sets show the effectiveness of the proposed method.

Brain tumors are among the main causes of cancer-related mortality in humans. Early detection of brain tumors is a vital job in the medical task of diagnosis and cure planning for patients. The automatic detection greatly facilitates medical personnel. Magnetic resonance imaging (MRI) is an accepted imaging strategy for diagnosing brain tumors. Presently, deep learning approaches have proven effective in handling various computer vision problems, such as image classification, because of their high performance and also determine models that can learn and decide based on sample data. In this study, the deep transfer learning method, namely InceptionResNet-V2, ResNet50, MobileNet-V2, and VGG16, was used to compare and find the most suitable model for brain tumor detection from the public MRI dataset. Also, CLAHE was employed as an image enhancement technique to improve the quality of the image data set before being used as the model input. As a result, the suggested method performed a prediction accuracy of up to 100%.