Comprehensive Insights Into CNN-based Models for Brain Tumor Identification

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 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.

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 from MRI Images Using Different Machine Learning Techniques

This study proposes an explainable deep learning framework for the automated detection and multi-class classification of brain tumors from MRI images, addressing key challenges in diagnostic accuracy, generalizability, and clinical interpretability. The framework employs a transfer learning–based Convolutional Neural Network (CNN) using the VGG16 architecture, fine-tuned on a balanced dataset comprising 6,484 MRI images collected from three publicly available repositories Figshare, SARTAJ, and Br35H. The dataset includes four classes: glioma, meningioma, pituitary, and no tumor, with equal class representation to ensure unbiased learning. Preprocessing was performed using the Python Imaging Library (PIL) to resize, normalize, and enhance image quality, while Kera's-based data augmentation introduced random variations in brightness and contrast to improve robustness against overfitting. The fine-tuned VGG16 model, with frozen early convolutional layers and retrained dense layers, achieved an overall classification accuracy of 95%, outperforming comparable deep architectures such as ResNet50 (93.5%), DenseNet121 (94%), and InceptionV3 (93%).Comprehensive performance evaluation through precision, recall, F1-score, and ROC–AUC analysis confirmed consistent multi-class discrimination, achieving a macro-average AUC of 0.97. Furthermore, Grad-CAM visualizations provided clear, class- specific heatmaps highlighting tumor-affected regions, thereby enhancing model transparency and diagnostic reliability. The integration of quantitative performance metrics with visual interpretability demonstrates that the proposed VGG16- based framework delivers clinically explainable, efficient, and accurate diagnostic support, reducing inter-observer variability and assisting radiologists in early brain tumor detection and treatment planning.

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.

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.

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.

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.

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.

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.

A carboxylesterase-activated "dual-locked" fluorescent probe for high-specificity imaging of hepatic tumors.

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

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.

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.