Predicting vulnerability for brain tumor: data-driven approach utilizing machine learning

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.

Early detection of brain tumors via MRI is crucial for improving patient outcomes. This study investigates a lightweight machine learning approach for multiclass brain tumor classification (glioma, meningioma, pituitary tumor, or no tumor) using Histogram of Oriented Gradients (HOG) for feature extraction and a Support Vector Machine (SVM) classifier. This study utilizes the public Brain Tumor Classification MRI Kaggle dataset, consisting of 2870 training and 394 testing MRI images across four classes. After converting the MRIs to grayscale and resizing them to 16×16 pixels, this study extracts HOG features and applies Principal Component Analysis (PCA) to retain 98% of the variance. An SVM is then trained with a GridSearchCV-optimized kernel and hyperparameters, and a custom class-weighted variant is compared. The best model, a polynomial-kernel SVM with custom class weights, achieved 91.8% test accuracy (95% CI (confidence interval): 90.9-92.7) with an F1-score of 0.919 ± 0.01, outperforming the best unweighted SVM (accuracy 86.0% ± 0.02, F1≈0.847). These results demonstrate that HOG+SVM, with proper weighting for class imbalance, can effectively classify brain tumors on small datasets at low computational cost. The novelty of this work lies in demonstrating that an optimized, class-weighted SVM leveraging compact HOG-PCA features can deliver over 91.8% accuracy with strong generalization on small-scale MRI data, providing a viable and interpretable alternative to complex Convolutional Neural Network (CNN) models. Future work can explore CNN and hybrid feature fusion to improve accuracy and generalization further.

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.

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.

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.

A brain tumor is the growth of abnormal cells in the tissues of brain. It affects a large number of people of different ages, worldwide. Magnetic Resonance Imaging (MRI) is the most operative and widely used technique for brain tumor detection because it provides better contrast images of the brain. However, the complexity of the problem, manual classification process, requirement of skilled medical practitioners, and a huge amount of MRI scan data are the major factors thwarting the timely classification of tumor vs. non-tumor. Early detection of brain tumors is possible by accurately applying machine learning with the aim to save time, cost, and human life. Recently, deep machine learning via transfer learning techniques was found to be highly effective for classification tasks. A tumor diagnosis application is presented with a VGG-19-based deep learning model by applying transfer learning of knowledge. Five-fold cross-validation of the model demonstrated 88% accuracy along with a 0.881 F1 score. The application could be utilized as a successful tool aid for oncologists and radiologists in the clinical diagnostics process.

Early detection of brain tumor is of critical importance in medical imagery. It reviews the application of advanced learning algorithms to increase the accuracy and efficiency of brain tumor detection using noninvasive imaging (in particular MRI). In the last couple of years, recent machine learning and deep learning approaches like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid models have shown an overnight progress by automating the process of extraction, segmentation, and classification of brain tumors. Tumor sub-region identification and feature selection has been improved by using techniques such as Random Forest classifiers, unsupervised clustering, and ensemble methods. Secondly, detection performance has been improved by the integration of handcrafted and automatic features, including texture, shape, and intensity. Further feature selection process is dimensionality reduction based techniques like Principal Component Analysis (PCA) and Information Gain (IG). The focus of this review is on the increasing usefulness of these algorithms to achieve adequate diagnosis and discuss future trends in personalized diagnostics and treatment planning.

One of the most common life-threatening diseases, the brain tumor is a condition characterized by the rapid proliferation of abnormal cells that leads to the destruction of healthy brain cells. Its aggressive nature can result in a patient succumbing to the disease before an accurate diagnosis is achieved. Timely detection is crucial to effective treatment and patient survival. Similarly, early detection plays a pivotal role in the case of brain tumors, where swift identification is vital to providing optimal care and increasing the chances of patient recovery. Streamlining the complex process of brain tumor detection is a significant undertaking that aims to simplify and expedite the procedure, ultimately contributing to saving valuable time and enhancing patient outcomes. The proposed model, a modified VGG-16, facilitates faster and more accurate identification of abnormal brain cells, leading to early detection of brain tumors. A novel multihead self-attention mechanism is used in the modified VGG-16 architecture to improve tumor detection performance. The proposed model performs better than other state-of-the-art models, such as normal VGG-16, ResNet-50, and EfficientNet.

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.

Early detection of brain tumor is a challenging task that assists medical practitioners in disease diagnosis. This article presents a computer-assisted brain tumor detection scheme using empirical wavelet transform based local binary pattern variant features and ant-lion optimization. Firstly, the input magnetic resonance (MR) image is enhanced using brightness preserving dynamic fuzzy histogram equalization (DFHE) algorithm. Then, significant modes from the input brain MR image are extracted using empirical wavelet transform (EWT), and five different local binary variants are extracted from each EWT mode. Further, the ant-lion feature selection algorithm is employed to select discriminating and relevant features and remove redundant descriptors that lower feature dimensionality. Finally, a support vector machine (SVM) classifier is used to detect the input MR image as healthy or tumorous. The proposed algorithm performance is validated on two widely and publicly available databases using different statistical measures including sensitivity, specificity, precision, F1-measure, and accuracy. A substantial set of experiments are carried out to evaluate the effect of individual descriptors, descriptor fusion, number of EWT decomposition levels, and DFHE enhancement on the classification accuracy. The simulation results obtained confirm that the presented approach performance is better as compared to other existing methods in terms of detection rate.

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

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

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.

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).