Diagnosis Of Brain Tumors Research Articles

Brain Midline Approximation to Improve Symmetry Analysis of Brain CT Scans.

Analysis of the symmetry of the brain hemispheres at the level of individual structures and dominant tissue features has been the subject of research for many years in the context of improving the effectiveness of imaging methods for the diagnosis of brain tumor, stroke, and Alzheimer's disease, among others. One useful approach is to reliably determine the midline of the brain, which allows comparative analysis of the hemispheres and uncovers information on symmetry/asymmetry in the relevant planes of, for example, CT scans. Therefore, an effective method that is robust to various geometric deformations, artifacts, varying noise characteristics, and natural anatomical variability is sought. This paper presents a novel method of estimating the midline of the brain. It is based on a comprehensive analysis of brain volume in two main steps. First, the images are reoriented. Using the matched ellipse, a head mask model is determined, the parameters of which are used to estimate the shape of the skull. Based on this, a precisely localized center line of the brain is created using 2D cross-correlation calculated on the edge images of successive scans. The algorithm developed has been validated, and the results obtained are competitive with the best reported achievements. The main advantages of the described method are high resistance to head tilt, simplicity of implementation, and high efficiency. The method can be used in a wide range of applications to support medical imaging diagnostics. The results obtained showed a 92.9% success rate and a mean error equal to 1.2mm.

Early Diagnosis of Brain Tumor Using AI and Mathematical Modeling

Timely detection of Brain Tumors is crucial for improving patient outcomes and reducing mortality rates. This study explores the significance of early diagnosis and investigates the role of various diagnostic methods. Leveraging advancements in medical imaging, including MRI, CT scans, and PET scans, alongside blood tests measuring biomarkers such as glial fibrillary acidic protein (GFAP) and neuron-specific enolase (NSE), facilitates early detection of Brain Tumors. Furthermore, emerging technologies like artificial intelligence algorithms enhance the accuracy and efficiency of diagnosis by analyzing imaging data and identifying subtle abnormalities. Early detection not only offers patients more treatment options but also improves their prognosis. Therefore, it is crucial to raise awareness about the importance of regular screenings and symptom awareness to ensure early detection and timely intervention for Brain Tumors. Keywords: Brain tumor, Early detection, Artificial intelligence, Mathematical modeling, Medical imaging, Mammography, liver MRI, Machine learning, Deep learning, Screening strategies.

Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications. BT segmentation with MRI remains challenging despite advancements in image acquisition techniques. Accurate detection and segmentation are essential for proper diagnosis and treatment planning. This study aims to enhance BT detection and segmentation accuracy and effectiveness of categorization through the implementation of an advanced stacking ensemble learning (SEL) approach. This study explores the efficiency of SEL architecture in augmenting the precision of BT segmentation. SEL, a prominent approach within the machine learning paradigm, combines the predictions of base-level models and improves the overall performance of predictions in order to reduce the errors and biases of each model. The proposed approach involves designing a stacked DenseNet201 as the meta-model called SEL-DenseNet201, complemented by six diverse base models such as mobile network version 3 (MobileNet-v3), 3-dimensional convolutional neural network (3D-CNN), visual geometry group network with 16 and 19 layers (VGG-16 and VGG-19), residual network with 50 layers (ResNet50), and Alex network (AlexNet). The strengths of the base models are calculated to capture distinct aspects of the BT MRI, aiming for enhanced segmentation performance. The proposed SEL-DenseNet201 is trained using BT MRI datasets. The augmentation techniques are applied to MRI scans to balance and enhance the model performance through the application of image enhancement and segmentation techniques. The proposed SEL-DenseNet201 achieves impressive results with an accuracy of 99.65% and a dice coefficient of 97.43%. These outcomes underscore the superiority of the proposed model over existing approaches. This study holds the potential to be an initial screening approach for early BT detection, with a high success rate.

"Synthetic" DSC Perfusion MRI with Adjustable Acquisition Parameters in Brain Tumors Using Dynamic Spin-and-Gradient-Echo Echoplanar Imaging.

Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics. Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ = 0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction. Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization.

Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects. Despite advancements in surgery and chemoradiotherapy, the prognosis for glioblastoma multiforme (GBM) and brain metastases remains poor, underscoring the need for innovative diagnostic strategies. This review highlights recent advancements in imaging techniques, liquid biopsies, and artificial intelligence (AI) applications addressing current diagnostic challenges. Advanced imaging techniques, including diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS), improve the differentiation of tumor progression from treatment-related changes. Additionally, novel positron emission tomography (PET) radiotracers, such as 18F-fluoropivalate, 18F-fluoroethyltyrosine, and 18F-fluluciclovine, facilitate metabolic profiling of high-grade gliomas. Liquid biopsy, a minimally invasive technique, enables real-time monitoring of biomarkers such as circulating tumor DNA (ctDNA), extracellular vesicles (EVs), circulating tumor cells (CTCs), and tumor-educated platelets (TEPs), enhancing diagnostic precision. AI-driven algorithms, such as convolutional neural networks, integrate diagnostic tools to improve accuracy, reduce interobserver variability, and accelerate clinical decision-making. These innovations advance personalized neuro-oncological care, offering new opportunities to improve outcomes for patients with central nervous system tumors. We advocate for future research integrating these tools into clinical workflows, addressing accessibility challenges, and standardizing methodologies to ensure broad applicability in neuro-oncology.

Revolutionizing Brain Tumor Diagnosis: A Comprehensive Model Integrating VGG19 and LSTM for Accurate MRI Classification

Diagnosing brain tumors is particularly difficult because they can grow in unpredictable ways and look very different on MRI scans. The current methods used to automatically identify these tumors often struggle because of the wide variety of tumor types and the complex structure of the brain. As a result, these methods don’t always classify tumors accurately, which can affect patient treatment and outcomes. The main problems with these methods are that they find it hard to distinguish between different types of tumors accurately and to deal with the various ways tumors can appear on MRI scans. To improve this situation, our study integrates the robust image classification capabilities of VGG19 with the sequential data processing strengths of LSTM. This synergistic approach enhances our model’s ability to accurately classify various types of brain tumors from MRI scans, addressing the inherent challenges associated with tumor heterogeneity in medical imaging. VGG19, a deep convolutional neural network, is employed to extract detailed features from MRI scans, facilitating precise tumor characterization based on visual patterns and LSTM complements VGG19 by capturing temporal dependencies in the sequential data of MRI scans, enabling the model to discern subtle variations in tumor appearances over time. By leveraging the combined power of VGG19 and LSTM architectures, our study achieves significant advancements in the accurate classification of brain tumors from MRI images. This approach not only enhances diagnostic precision but also lays the groundwork for future improvements in neuro-oncological imaging diagnostics. Our study includes 1000 patients evaluated with MRI for brain tumors. We achieved an overall accuracy of 98.32% demonstrating the efficacy of our VGG19-LSTM model in accurate tumor classification. By using both, our model aims to get better at understanding MRI scans and, as a result, be more accurate at identifying brain tumors. This combination is a new step forward in making brain tumor diagnosis more precise through a detailed and cooperative approach using neural networks.

Financial burden after brain tumor diagnosis – the cost of disease for patients and caregivers

Financial burden after brain tumor diagnosis – the cost of disease for patients and caregivers

A Heuristic Strategy Assisted Deep Learning Models for Brain Tumor Classification and Abnormality Segmentation

ABSTRACTBrain tumors are prevalent forms of malignant neoplasms that, depending on their type, location, and grade, can significantly reduce life expectancy due to their invasive nature and potential for rapid progression. Accordingly, brain tumors classification is an essential step that allows doctors to perform appropriate treatment. Many studies have been done in the sector of medical image processing by employing computational methods to effectively segment and classify tumors. However, the larger amount of information collected by healthcare images prohibits the manual segmentation process in a reasonable time frame, reducing error measures in healthcare settings. Therefore, automated and efficient techniques for segmentation are crucial. In addition, various visual information, noisy images, occlusion, uneven image textures, confused objects, and other features may impact the process. Therefore, the implementation of deep learning provides remarkable results in medicinal image processing, particularly in the segmentation and classification process. However, conventional deep learning‐assisted methods struggle with complex structures and dimensional issues. Thus, this paper develops an effective technique for diagnosing brain tumors. The main aspect of the proposed system is to classify the brain tumor types by segmenting the affected regions of the raw images. This novel approach can be applied for various applications like diagnostic centers, decision‐making tools, clinical trials, medical research institutes, disease prognosis, and so on. Initially, the requisite images are collected from standard datasets and further, it is subjected to the segmentation period. In this stage, the Multi‐scale and Dilated TransUNet++ (MDTUNet++) model is employed to segment the abnormalities. Further, the segmented images are given into an Adaptive Dilated Dense Residual Attention Network (ADDRAN) to classify the brain tumor types. Here, to optimize the ADDRAN technique's parameters, an Improved Hermit Crab Optimizer (IHCO) is supported, which increases the accuracy rates of the overall network. Finally, the numerical examination is conducted to guarantee the robustness and usefulness of the designed model by contrasting it with other related techniques. For Dataset 1, the accuracy value attains 93.71 for the proposed work compared to 87.86 for CNN, 90.18 for DenseNet, and 89.56 and 90.96 for RAN and DRAN, respectively. Thus, supremacy has been achieved for the recommended system while detecting the brain tumor types.

Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI.

Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades. Our system employs ensemble deep learning (EDL) within an MRI multiclass framework that includes five datasets: two-class (C2), three-class (C3), four-class (C4), five-class (C5) and six-class (C6). The EDL utilizes a majority voting algorithm to classify brain tumors by combining seven renowned deep learning (DL) models-EfficientNet, VGG16, ResNet18, GoogleNet, ResNet50, Inception-V3 and DarkNet-and seven machine learning (ML) models, including support vector machine, K-nearest neighbour, Naïve Bayes, decision tree, linear discriminant analysis, artificial neural network and random forest. Additionally, local interpretable model-agnostic explanations (LIME) are employed as an explainable AI algorithm, providing a visual representation of the CNN's internal workings to enhance the credibility of the results. Through extensive five-fold cross-validation experiments, the DL-based majority voting algorithm outperformed the ML-based majority voting algorithm, achieving the highest average accuracies of 100 ± 0.00%, 98.55 ± 0.35%, 98.47 ± 0.63%, 95.34 ± 1.17% and 96.61 ± 0.85% for the C2, C3, C4, C5 and C6 datasets, respectively. Majority voting algorithms typically yield consistent results across different folds of the brain tumor data and enhance performance compared to any individual deep learning and machine learning models.

Computer Aided Brain Tumor Diagnosis using Coati Optimization Algorithm with Explainable Artificial Intelligence Approach

Brain tumors (BT) are a difficult and dangerous medical condition, and the accurate and early analysis of these tumors is crucial for suitable treatment. Explainability in clinical image diagnosis role a vital play in the correct analysis and treatment of tumors that supports medical staff's optimum understanding of the image analysis performances rely upon deep methods. Artificial intelligence (AI), in certain deep neural networks (DNNs) has attained remarkable outcomes for clinical image analysis in many applications. However, the need for explainability of deep neural approaches has been assumed that major restriction before executing these approaches in medical practice. Explainable AI, or XAI, is a vital module in this context as it supports medical staff and patients in understanding the AI's decision-making model, enhancing trust and transparency. It leads to optimum patient care and performance but making sure that medical staff can make learned decisions depends on AI-driven insights. Therefore, this study develops a novel Computer-Aided Brain Tumor Diagnosis using Coati Optimization Algorithm with an Explainable Artificial Intelligence (CABTD-COAXAI) approach. The purpose of the CABTD-COAXAI technique is to exploit XAI and hyperparameter-tuned deep learning (DL) approaches for automated BT analysis. To accomplish this, the CABTD-COAXAI technique follows a Gaussian filtering (GF) based noise removal process. Besides, the CABTD-COAXAI technique utilizes the EfficientNetB7 methods for the feature extraction process. Additionally, the hyperparameter tuning of the EfficientNetB7 method is performed by the use of COA. Furthermore, the classification of the BT process can be performed by the usage of a convolutional autoencoder (CAE). Finally, the CABTD-COAXAI system combines the XAI method named LIME to effectively understand and explainability of the black-box model for automated BT diagnosis. The simulation result of the CABTD-COAXAI technique has been tested on a benchmark BT database. The extensive outcomes inferred that the CABTD-COAXAI method reaches superior performance over other models in terms of different measures

Longitudinal Psychological Distress After Malignant Brain Tumor Diagnosis: A Multilevel Analysis of Patients and Their Caregivers.

Malignant brain tumors are associated with debilitating symptoms and a poor prognosis, resulting in high psychological distress for patients and caregivers. There is a lack of longitudinal studies investigating psychological distress in this group. This study evaluated fear of progression (FoP), anxiety and depression in patients and their caregivers in the 6months following malignant brain tumor diagnosis. This prospective, observational study assessed FoP (FoP-Q-SF[P]), anxiety and depression (HADS) at diagnosis (T0) and after three (T1) and 6months (T2) in patients with malignant brain tumors (primary, secondary) and their caregivers. Multilevel analyses were used to examine changes over time and differences between patients and caregivers, while accounting for the interdependence in their distress values. Seventy-one patients and 68 caregivers were included in the analysis. Throughout the study period, over 50% reported clinically relevant FoP, almost 50% reported clinically relevant anxiety, and over 30% reported relevant depression. Over all time points, caregivers reported significantly higher anxiety and depression than patients. Anxiety decreased between T0 and T2 in both groups. Exploratory analyses showed that female sex was associated with higher anxiety, and older age with higher depression. No significant predictors were identified for FoP. A substantial number of patients and caregivers experience clinically relevant psychological distress in the 6months following a malignant brain tumor diagnosis. Caregivers are particularly distressed, reporting higher anxiety and depression. Integrating psycho-oncological assessments and interventions for both patients and caregivers into clinical care is critical to address the psychological distress associated with malignant brain tumors.

Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects

Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects

Artificial Intelligence (AI): Brain Tumor Detection

The detection and diagnosis of brain tumors, a critical medical challenge, have greatly benefited from the application of Artificial Intelligence (AI). This review paper explores the advancements, methods, and technologies of AI in the detection and classification of brain tumors from medical imaging modalities. It also highlights the importance of machine learning (ML) and deep learning (DL) algorithms, particularly Convolutional Neural Networks (CNNs), in improving diagnostic accuracy, early detection, and prognosis prediction. Moreover, the paper addresses challenges and future directions in integrating AI with clinical practices for brain tumor management.

Machine Learning in Brain Tumor Diagnosis: Assessing the Efficacy of Diverse Methods

Background: Brain tumors constitute a critical and potentially life-threatening condition that requires accurate and timely diagnosis. In recent years, machine learning (ML) approaches have emerged as powerful tools for assisting radiologists and clinicians in identifying and classifying tumors on medical imaging. While numerous ML methods, including conventional machine learning algorithms and deep learning-based models, have been explored, a comprehensive analysis of their efficacy in detecting, segmenting, and classifying brain tumors remains essential. Methods: We conducted a systematic evaluation of diverse ML methods used in brain tumor diagnosis. We first identified and collated relevant studies from major scientific databases, focusing on image-based applications such as magnetic resonance imaging (MRI). The methods included pre-processing pipelines, feature extraction techniques, and both supervised and unsupervised learning approaches. We then compared these methods based on standard metrics, including accuracy, sensitivity, specificity, and F1-score, to gain insights into their relative performance. Furthermore, we applied representative algorithms (such as support vector machines, convolutional neural networks, and random forests) to an open-access brain tumor imaging dataset to evaluate their performance and compare empirical findings. Results: Results indicated that deep learning models, particularly convolutional neural networks, consistently outperformed traditional ML models across various performance metrics. In particular, automated feature extraction in deep learning approaches proved pivotal in capturing nuanced information such as tumor shape and heterogeneity. Conventional algorithms still demonstrated merit when dataset sizes were limited or when computational resources were constrained. Additionally, various data augmentation techniques improved the robustness and generalizability of ML models in situations with scarce annotated data. Conclusion: Our findings suggest that deep learning-based methods hold the greatest potential for accurate and efficient brain tumor diagnosis, particularly when coupled with robust datasets and high-quality imaging. Nonetheless, careful selection of model architecture, training strategies, and validation protocols remains paramount to ensure reliable clinical translation.

Clinical confocal laser endomicroscopy for imaging of autofluorescence signals of human brain tumors and non-tumor brain

PurposeAnalysis of autofluorescence holds promise for brain tumor delineation and diagnosis. Therefore, we investigated the potential of a commercial confocal laser scanning endomicroscopy (CLE) system for clinical imaging of brain tumors.MethodsA clinical CLE system with fiber probe and 488 nm laser excitation was used to acquire images of tissue autofluorescence. Fresh samples were obtained from routine surgeries (glioblastoma n = 6, meningioma n = 6, brain metastases n = 10, pituitary adenoma n = 2, non-tumor from surgery for the treatment of pharmacoresistant epilepsy n = 2). Additionally, in situ intraoperative label-free CLE was performed in three cases. The autofluorescence images were visually inspected for feature identification and quantification. For reference, tissue cryosections were prepared and further analyzed by label-free multiphoton microscopy and HE histology.ResultsLabel-free CLE enabled the acquisition of autofluorescence images for all cases. Autofluorescent structures were assigned to the cytoplasmic compartment of cells, elastin fibers, psammoma bodies and blood vessels by comparison to references. Sparse punctuated autofluorescence was identified in most images across all cases, while dense punctuated autofluorescence was most frequent in glioblastomas. Autofluorescent cells were observed in higher abundancies in images of non-tumor samples. Diffuse autofluorescence, fibers and round fluorescent structures were predominantly found in tumor tissues.ConclusionLabel-free CLE imaging through an approved clinical device was able to visualize the characteristic autofluorescence patterns of human brain tumors and non-tumor brain tissue ex vivo and in situ. Therefore, this approach offers the possibility to obtain intraoperative diagnostic information before resection, importantly independent of any kind of marker or label.

Artificial Intelligence (AI) for Brain Tumor Detection: Automating MRI Image Analysis for Enhanced Accuracy

Accurately diagnosing and planning for the treatment of brain tumors is crucial in clinical practice. Brain tumor detection and diagnosis rely heavily on artificial intelligence (AI) systems that mainly employ medical imaging modalities like MRI. This study employs cutting-edge DL and image processing techniques to intelligently forecast the brain tumor using AI. The complicated and varied nature of brain tumors frequently presents challenges to deep learning models, despite their promising performance in this task. In order to overcome this obstacle, we present the InceptionV3 architecture, which is based on CNNs and uses 5-fold cross-validation to classify brain tumors from MRI images. A training, validation, and testing of a model were conducted using a publically accessible MRI dataset that included 7023 greyscale brain MRI pictures. These images were classified into four types of tumors: gliomas, meningiomas, no tumors, and pituitary. To enhance diversity of a training dataset, the photos were preprocessed by scaling, greyscale conversion, and labeling. Afterward, data augmentation techniques were applied. A model's performance was assessed using 5-fold cross-validation, yielding an F1-score of 99.98%, an average accuracy of 97.12%, precision of 97.97%, and recall of 96.59%. Other Artificial Intelligent models that were compared included InceptionV3, VGG19, CNN, and DenseNet and the results indicated that the InceptionV3 gave better results overall. These results demonstrate that deep learning can accurately and efficiently detect brain tumors utilizing MRI pictures.

Advancing Brain Tumour Detection and Classification: Knowledge Distilled ResNeXt Model for Multi-Class MRI Analysis

Accurate and timely diagnosis of brain tumors is crucial for optimal patient outcomes. Despite advancements in medical imaging and deep learning, the accurate classification of brain tumors remains a significant challenge. Existing methods, including CNNs and VGG16, often struggle to differentiate between tumor types and capture subtle radiological features. To address these limitations, we propose a novel Knowledge Distilled ResNeXt architecture. By transferring knowledge from a complex teacher model, our model effectively learns discriminative features and improves classification accuracy. Our comprehensive experiments demonstrate the superiority of the Knowledge Distilled ResNeXt in classifying brain tumors (glioma, meningioma, pituitary tumor, and no tumor) compared to state-of-the-art methods. This research contributes to the development of more effective diagnostic tools and improved patient care.

Breakthrough in Brain Tumor Diagnosis: A Cutting-Edge Hybrid Depthwise-Direct Acyclic Graph Network for MRI Image Classification

Both adults and children are at risk of dying from brain tumors. On the other hand, prompt and precise detection can save lives. Early detection is necessary for a proper diagnosis of a brain tumor, and magnetic resonance imaging (MRI) is often used in this context. To assist in the early diagnosis of sickness, neuro-oncologists have used Computer-Aided Diagnosis (CAD) in a number of ways. In this study, proposed a hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET)-based deep learning was developed to distinguish between cancers and non-tumors. Three processes make up this method: pre-processing, classification, and feature extraction. Pre-processing methods used in this study included contrast enhancement and image shrinking. The MRI picture is processed to get the wavelet and texture properties and used to build a classifier. Using MRI scans, the proposed hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model classifies two types of brain tumors: tumor and non-tumor. Performance criteria such as accuracy (ACC), specificity (SP), and sensitivity (SE) are used to assess the suggested hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model. Based on 850 images, the studies yielded a 99.54% categorization accuracy demonstrate that the suggested hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model beats the most advanced methods.

Cortically Based Brain Tumors in Children: A Decision-Tree Approach in the Radiology Reading Room.

Cortically based brain tumors in children constitute a unique set of tumors with variably aggressive biologic behavior. Because radiologists play an integral role on the multidisciplinary medical team, a clinically useful and easy-to-follow flow chart for the differential diagnoses of these complex brain tumors is essential. This proposed algorithm tree provides the latest insights into the typical imaging characteristics and epidemiologic data that differentiate the tumor entities, taking into perspective the 2021 World Health Organization's classification and highlighting classic as well as newly identified pathologic subtypes by using current molecular understanding.

Weakly supervised brain tumour segmentation with label propagation and level set loss

AbstractEarly diagnosis of brain tumors significantly enhances treatment success. However, accurate detection and segmentation of tumors, essential for diagnosis, rely heavily on costly manual annotation by experts. To mitigate these costs, weakly supervised methods have gained traction. This paper introduces a novel weakly supervised brain tumor segmentation approach utilizing point and scribble supervision. Experts annotate only the slice with the largest tumor area by marking a single point near the tumor center or drawing a scribble within the tumor region. The method operates in two phases. First, labels are propagated to unlabelled pixels, generating a pseudo‐ground‐truth with three labels: tumor, non‐tumor, and marginal pixels (unlabelled pixels surrounding the initial segmentation). Second, a segmentation model is trained using the pseudo‐ground‐truth and a loss function combining level‐set and binary cross‐entropy losses. Marginal pixels contribute to level‐set loss computation, refining the segmentation process. The approach is validated on 3D magnetic resonance imaging (MRI) volumes from BraTS2020, BraTS2021, and BraTS2023 benchmark datasets. Experimental results show Dice scores comparable to fully supervised methods for whole tumor segmentation, demonstrating the effectiveness of the proposed weakly supervised strategy. This method reduces annotation effort while maintaining competitive segmentation performance, making it valuable for clinical applications.