Multiclass Brain Tumor Detection using EfficientNet B3 Model with Magnetic Resonance Imaging

Background: Classification of brain tumors is an integral aspect of medical imaging studies as different tumor features require multiclassification which aids in making the diagnosis. However, this task is of great difficulty owing to the nature of the brain MRI images. Recent deep learning (DL) models have made it possible to have a brain tumor classification with high accuracy, which is very useful to neurologists. Based on these new capabilities, this study intends to enhance brain tumor detection with the help of a hybrid transfer learning approach. Methods: This study developed a brain tumor diagnosis system using five advanced DL architectures: Xception, ResNet164, DenseNet121, DenseNet201, and Inception-ResNetV2. A hybrid DL model was created by integrating a deep-dense block and a softmax activation function in the final layers of these architectures. The proposed approach enhances classification accuracy and precision. Two experiments were conducted: Three-class classification, involving images from patients with gliomas, meningiomas, and pituitary tumors. Four-class classification, which included gliomas, meningiomas, pituitary tumors, and normal brain images. Results: The Xception architecture emerged as the most effective model in detecting brain tumors when implemented with the proposed hybrid DL modifications. The system achieved an accuracy of 99.71% on the three-class dataset and 96.17% on the four-class dataset, outperforming state-ofthe- art methods in brain tumor classification. Conclusion: This research shows that hybrid DL models can enhance the capabilities of brain tumor classification systems in the context of complex MRI datasets. The optimization of activation functions and the inclusion of deep-dense blocks increase both accuracy and precision, providing adequate solutions for neurologists. These results lead to the recognition of the Xception-based hybrid model as a robust and efficient model for detecting brain tumors.

Categorizing brain cancers, including glioma, meningioma, and pituitary tumors, based on magnetic resonance imaging (MRI) images presents a significant challenge. Deep learning and machine learning techniques have shown promise in enhancing image categorization. To address this challenge, we leverage the power of the optimized ResNet50 model. Our approach involves classifying medical images using Convolutional Neural Network (CNN) features, which are then compared with the ResNet50 model. The primary goal is to detect brain tumors at an early stage using an advanced deep-learning model. We utilize an accessible dataset from Figshare, containing MRI images of the three distinct categories of brain tumors. Existing brain tumor models face limitations in handling multi-class problems and early-stage diagnosis. Therefore, we propose a fully automated approach employing Convolutional Neural Networks (CNN) to extract diverse properties from brain MRI scans. This method aims to provide accurate tumor diagnosis, even with a high number of classes and limited information in MRI data. Our proposed model involves the creation of identification blocks within a four-layered primary architecture, followed by testing and assessment of the interconnected layers. The results demonstrate that our model outperforms existing methods, achieving an impressive overall classification accuracy of 99.03%,

In recent years, Magnetic Resonance Imaging (MRI) scanning has been the most rapidly developing field. Concerning the tumor’s size and specifics, diagnosing and classifying brain tumor’s is challenging and time-consuming for radiologists. The growth of abnormal cells in the brain is referred to as a brain tumor. A brain tumor is diagnosed in about 11,700 patients every year. It is estimated that 34% of males and 36% of females will survive five years after being diagnosed with malignant brain or different tumors. This study focuses on meningioma, pituitary, glioma and no tumors’, among the many brain tumors. Deep learning algorithms and machine learning methods were used to create an autonomous classification and segmentation system for brain tumors, significantly improving early detection. Using the Visual Geometry Group (16), parameters were set for training the model that detects brain tumors based on analysis of proposed literature solutions. Simple Convolution Neural Network (CNN) models such as VGG-16 and Efficient NetB7 perform well because they are among the highest-performing models. As a result of the study, quick, efficient, and precise decisions can be made using MRI to detect brain tumors. For this 7022 brain magnetic resonance images were used to train and test this model. According to experimental findings, the suggested differential deep CNN model could accurately categories MRI pictures of brain tumors, including aberrant and standard images, with a 98.19% accuracy rate.

Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and neck cancers (HNCs) and brain tumors in MRI images. PubMed, Web of Science, Embase, and Scopus were searched to retrieve related studies published from January 2016 to January 2020. Studies that evaluated the performance of DL-based models in the segmentation, and/or classification and/or grading of HNCs and/or brain tumors were included. Selected studies for each analysis were statistically evaluated based on the diagnostic performance metrics. The search results retrieved 1,664 related studies, of which 30 studies were eligible for meta-analysis. The overall performance of DL models for the complete tumor in terms of the pooled Dice score, sensitivity, and specificity was 0.8965 (95% confidence interval (95% CI): 0.76-0.9994), 0.9132 (95% CI: 0.71-0.994) and 0.9164 (95% CI: 0.78-1.00), respectively. The DL methods achieved the highest performance for classifying three types of glioma, meningioma, and pituitary tumors with overall accuracies of 96.01%, 99.73%, and 96.58%, respectively. Stratification of glioma tumors by high and low grading revealed overall accuracies of 94.32% and 94.23% for the DL methods, respectively. Based on the obtained results, we can acknowledge the significant ability of DL methods in the mentioned applications. Poor reporting in these studies challenges the analysis process, so it is recommended that future studies report comprehensive results based on different metrics.

Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges of low accuracy and high false-positive results. The model presented in this study, which offers improvements for feature extraction and classification, uses deep learning and machine learning for the assessment of brain tumors. Deep learning is used for feature extraction and encompasses the application of different models such as fine-tuned Inception-v3 and fine-tuned Xception. The classification of brain tumors is explored through deep- and machine-learning algorithms including softmax, Random Forest, Support Vector Machine, K-Nearest Neighbors, and the ensemble technique. The results of these approaches are compared with existing methods. The Inception-v3 model has a test accuracy of 94.34% and achieves the highest performance compared with other recently reported methods. This improvement may be sufficient to support a significant role in clinical applications for brain tumor analysis. Furthermore, this type of approach can be used as an effective decision-support tool for radiologists in medical diagnostics as a second opinion based on the magnetic resonance imaging (MRI) analysis. It may also save valuable time for radiologists who have to manually review numerous MRI images of patients.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.

Introduction: Brain tumors pose a significant danger to human health due to their complexity, variety and ability to progressive progress. Early and accurate diagnosis is important for effective treatment and improvement in survival rates. Traditional clinical processes are often dependent on time programs, subjective and expensive clinical equipment. The project presents an integrated, AI operated, multi-phase system for brain tumor detection and recommendations of personal treatment. By combining symptomatic evaluation, biomarker analysis, advanced imaging interpretation and machine learning models, this system aims to help health professionals with quick and more accurate clinical decisions.Objectives: The main objective of this project is to develop an integrated, multi -phase system for early detection and personal treatment using a combination of clinical symptoms, molecular biomarkers, imaging and artificial intelligence. The system begins to evaluate patient -reported symptoms and risk factors to calculate a functional weighted risk index (FWRI) for initial risk assessment. It then analyzes a blood -based biomarker to generate a blood -based tumor signal index (BTSI), which provides insight at molecular level. Then it explains the ATR-FTIR and the Multi-Omics report through rule-based arguments for determining nonconformities. The MRI scan is then classified into tumors-types-glioma, meningioma, pituitary or no tumor-a fine- model VGG19 uses deep learning models. Finally, the system integrates the results of all previous stages to offer individual means based on tumor type, severity and general risk profile.Methods: The proposed system follows a sequential five-stage methodology for comprehensive brain tumor detection and remedy making plans. In Stage 1, users input symptom severity and history through an interactive interface, which calculates a Functional Weighted Risk Index (FWRI) to assess preliminary tumor chance. Stage 2 entails the input of blood biomarker ranges, which might be analyzed to generate a Blood-Based Tumor Signal Index (BTSI), reflecting molecular signs of tumor presence. Stage 3 allows users to add ATR-FTIR or Multi-Omics reviews,that are interpreted using rule-primarily based logic to identify unusual styles that warrant imaging. In Stage 4, customers add brain MRI scans which can be processed through a pre-trained VGG19 convolutional neural network to classify the tumor into one among four classes: glioma, meningioma, pituitary, or no tumor. Finally, Stage five combines the outputs from all previous levels to generate customized treatment tips, taking into consideration the identified tumor type, severity, biomarker signals, and medical danger ratingsResults: The multi -phase system demonstrated promising consequences in all components. In phase 1, FWRI is effectively stable in categories with low, medium and high risks based on symptom input and severity. Phase 2 has shown that BTSI values have become close in line with a biomarker threshold installed, which strengthens the system's ability to reflect molecular tumor designs. Phase 3 explained ATR-FFIR and Multi-Omics data successfully, the exact flag of the unusual profile for further imaging analysis. In Step 4, VGG19 was based MR classification model accuracy of more than 92%, which distinguishes well between glioma, meningoom, pituitary tumor and no tumor. Finally, step 5 recommendations generated clinically relevant and similar relevant medical guidelines, which demonstrate the system's ability to provide integrated, computer-driven support for individual brain tumor care.Conclusions: Finally, integrated five-phase brain tumor detection and treatment system effectively combine the evaluation of clinical symptoms, biomorker analysis, omics data interpretation, advanced MRI classification and individual treatment form into an integrated diagnostic pipeline. By taking advantage of rule -based arguments and deep teaching techniques, the system increases early identification accuracy, reduces clinical delays and supports data driven clinical decision -making. The sequential current ensures that each step does on the previous one, reduces false positive and optimizing patient -specific consequences. With further clinical verification, this AI-assisted framework has a strong ability to become a reliable and skilled tool in medical environments in the real world for early diagnosis and handling of brain tumors.

BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization.

This paper proposes a method to detect and classify gliomas using Magnetic Resonance Imaging (MRI)... Brain tumors are severe diseases affecting over 200 varieties and have increased by 300% in the last three decades. Early detection and treatment are crucial for patient survival. Magnetic Resonance Imaging (MRI) is the most commonly used method for diagnosing brain tumors. Advances in machine learning, particularly deep learning, have led to the identification and classification of medical imaging patterns. Convolutional Neural Networks (CNNs) and autoencoders are successful techniques in image processing for high diagnostic accuracy. Various deep learning and machine learning algorithms have been used to identify tumors and detect cardiovascular stenosis, with high diagnostic accuracy. However, many studies have limitations, such as a lack of performance comparison between proposed models and traditional machine learning methods. Brain tumors are diseases caused by abnormal cell growth in the brain, with non-cancerous and malignant types.

Continuous advancements in deep learning are affecting various research areas, especially research on applications in the medical sector. A computer-aided diagnosis system that utilizes deep learning is used for classifying and detecting brain tumors in magnetic resonance imaging. Regarding brain tumors, the main diagnostic indicators are patient symptoms and outcomes of magnetic resonance imaging. Frequent changes in the symptoms of these tumors have raised serious concerns about potential misdiagnoses. Implementing computer-aided diagnosis systems can support diagnostic methods that rely on the visual assessments of physicians, potentially reducing misdiagnosis rates. In this study, we propose an enhanced computer-aided diagnosis algorithm that is optimized for brain tumor classification. We removed noise from the magnetic resonance imaging results by applying Gaussian filters, and we employed GridMask to improve the generalization performances of the deep learning models. Then, we applied Patterned-GridMask, which is a method we proposed to reduce the issue of brain tumors being obscured by standard GridMask. Under the application of Patterned-GridMask, a performance improvement of up to 6% was demonstrated across the four deep learning models used in the experiments: ViT-B/16, MaxViT-B, TresNet-M, and EfficientNetV2-M, with the highest performance being represented by an accuracy and F1-score of 97.74% and 97.75%, respectively. Using the proposed computer-aided diagnosis system, improved diagnosis results can be obtained, thereby resulting in more accurate rates of early detection, better patient outcomes, and more appropriate treatment selection.

Brain tumor detection and segmentation from Magnetic Resonance Imaging (MRI) images is being one of the emerging fields in the biomedicine. A formidable undertaking in brain tumor surgery, medical care, treatment programme and quantitative assessment of MRI images is to precisely diagnose its location and extent. Recently, the convolutional neural network (CNN) based detection and segmentation method on brain tumor MRI images is being one of the emerging fields in the medical imaging as an automatic clinic treatment and evaluation solution. In this article, we put forward a brand new quadruplet loss in CNN framework, which achieves higher accuracy in brain tumor detection and segmentation than other pairwise loss and triplet loss methods. By applying the proposed quadruplet loss to the original L2Net CNN architecture leads to a more compact descriptor named QuadrupletNet. From our experiments, QuadrupletNet shows higher performance than other state-of-the-art loss functions e.g., the Triplet loss, as indicated in experiments on Multimodal Brain Tumor Image Segmentation (BRATS 2018) datasets, and on our own collected MRI brain tumor datasets (named MBTD).

Brain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stages thus enabling early treatment. The main non-invasive methods for brain tumor diagnosis and assessment are brain imaging like computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. In this paper, the focus is on detection and segmentation of brain tumors from 2D and 3D brain MRIs. For this purpose, a complete automated system with a web application user interface is described which detects and segments brain tumors with more than 90% accuracy and Dice scores. The user can upload brain MRIs or can access brain images from hospital databases to check presence or absence of brain tumor, to check the existence of brain tumor from brain MRI features and to extract the tumor region precisely from the brain MRI using deep neural networks like CNN, U-Net and U-Net++. The web application also provides an option for entering feedbacks on the results of the detection and segmentation to allow healthcare professionals to add more precise information on the results that can be used to train the model for better future predictions and segmentations.