Early Detection Of Brain Tumors Research Articles

Optimasi Deteksi Tumor Otak Menggunakan Adaptive Multiscale Retinex dan YOLOV10 Pada Citra Digital

The incidence of brain tumors in Indonesia continues to rise annually, affecting both adults and children. Therefore, an effective detection system is required to differentiate MRI images showing the presence of brain tumors from those that do not. This distinction is crucial in the analysis and diagnosis of medical images. This study aims to optimize brain tumor detection in digital images by combining the Adaptive Multiscale Retinex (AMSR) method with the YOLOv10 algorithm. AMSR enhances the quality and contrast of brain images, making critical details more visible. The method adjusts the scale and intensity of image lighting to address uneven illumination and improve the visibility of brain structures. YOLOv10 is recognized for its speed and accuracy in real-time object detection. This algorithm employs deep learning techniques to identify and classify objects with high precision on enhanced brain image datasets. The model's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and computation time. Results indicate that the combination of AMSR and YOLOv10 improves the accuracy and efficiency of brain tumor detection compared to conventional methods. The YOLOv10 model achieved a detection accuracy of 92%. This study provides significant contributions to early brain tumor detection technology, with important implications for the healthcare sector.

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

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

DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection

Brain tumors are the result of irregular development of cells. It is a major cause of adult demise worldwide. Several deaths can be avoided with early brain tumor detection. Magnetic resonance imaging (MRI) for earlier brain tumor diagnosis may improve the chance of survival for patients. The most common method of diagnosing brain tumors is MRI. The improved visibility of malignancies in MRI makes therapy easier. The diagnosis and treatment of brain cancers depend on their identification and treatment. Numerous deep learning models are proposed over the last decade including Alexnet, VGG, Inception, ResNet, DenseNet, etc. All these models are trained on a huge dataset, ImageNet. These general models have many parameters, which become irrelevant when implementing these models for a specific problem. This study uses a custom deep-learning model for the classification of brain MRIs. The proposed Disease and Spatial Attention Model (DaSAM) has two modules; (a) the Disease Attention Module (DAM), to distinguish between disease and non-disease regions of an image, and (b) the Spatial Attention Module (SAM), to extract important features. The experiments of the proposed model are conducted on two multi-class datasets that are publicly available, the Figshare and Kaggle datasets, where it achieves precision values of 99% and 96%, respectively. The proposed model is also tested using cross-dataset validation, where it achieved 85% accuracy when trained on the Figshare dataset and validated on the Kaggle dataset. The incorporation of DAM and SAM modules enabled the functionality of feature mapping, which proved to be useful for the highlighting of important features during the decision-making process of the model.

A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation.

Introduction: Brain tumors are abnormal cell growths in the brain, posing significant treatment challenges. Accurate early detection using non-invasive methods is crucial for effective treatment. This research focuses on improving the early detection of brain tumors in MRI images through advanced deep-learning techniques. The primary goal is to identify the most effective deep-learning model for classifying brain tumors from MRI data, enhancing diagnostic accuracy and reliability. Methods: The proposed method for brain tumor classification integrates segmentation using K-means++, feature extraction from the Spatial Gray Level Dependence Matrix (SGLDM), and classification with ResNet50, along with synthetic data augmentation to enhance model robustness. Segmentation isolates tumor regions, while SGLDM captures critical texture information. The ResNet50 model then classifies the tumors accurately. To further improve the interpretability of the classification results, Grad-CAM is employed, providing visual explanations by highlighting influential regions in the MRI images. Result: In terms of accuracy, sensitivity, and specificity, the evaluation on the Br35H::BrainTumorDetection2020 dataset showed superior performance of the suggested method compared to existing state-of-the-art approaches. This indicates its effectiveness in achieving higher precision in identifying and classifying brain tumors from MRI data, showcasing advancements in diagnostic reliability and efficacy. Discussion: The superior performance of the suggested method indicates its robustness in accurately classifying brain tumors from MRI images, achieving higher accuracy, sensitivity, and specificity compared to existing methods. The method's enhanced sensitivity ensures a greater detection rate of true positive cases, while its improved specificity reduces false positives, thereby optimizing clinical decision-making and patient care in neuro-oncology.

Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach

The treatment of brain tumors poses significant challenges and contributes to a significant number of deaths on a global scale. The process of identifying brain tumors in medical practice involves the visual analysis of photographs by healthcare experts, who manually delineate the tumor locations. However, this approach is characterized by its time-consuming nature and susceptibility to errors. In recent years, scholars have put forth automated approaches to early detection of brain tumors. However, these techniques face challenges attributed to their limited precision and significant false-positive rates. There is a need for an effective methodology to identify and classify tumors, which involves extracting reliable features and achieving precise disease classification. This work presents a novel model architecture that is derived from the EfficientNetB3. The suggested framework has been trained and assessed on a dataset consisting of 7,023 magnetic resonance images. The findings of this study indicate that the fused feature vector exhibits superior performance compared to the individual vectors. Furthermore, the technique that was provided showed superior performance compared to the currently available systems and attained a 100% accuracy rate. As a result, it is viable to employ this technique within a clinical environment for the purpose of categorizing brain tumors based on magnetic resonance images scans.

ABES: attention bi-directional ensemble SVM for early detection of brain tumors

ABES: attention bi-directional ensemble SVM for early detection of brain tumors

Enhanced Brain Tumor Classification: A Hybrid Classifier Approach

Early detection of brain tumors is crucial for enhancing patient survival rates and treatment effectiveness. Analyzing magnetic resonance imaging (MRI) images by manually is a difficult task. Therefore, there is a requirement for digital techniques to improve the accuracy of tumor diagnosis. Study of this work was to enhance the performance of Machine Learning classifiers rather than move to deep learning. Machine Learning classifiers are advantageous compared to deep learning methods because of their ability to train on small datasets, low computing time complexity, affordability, and ease of adoption by individuals with less expertise The proposed methodology encompasses image processing, segmentation, feature extraction, feature reduction, and classification, leveraging techniques such as Otsu’s thresholding, GLCM, BWT, and PCA to extract pertinent information while minimizing dimensionality. By integrating a hybrid classifier that combines SVM, DT, and KNN, along with ensemble techniques, significant improvements in classification accuracy and robustness are achieved. Experimental results on the TCGA-GBM dataset showcase accuracy of 96.9%, with high specificity (95.14%) and sensitivity (97.14%) for classify normal tumors (benign tumors) and cancerous tumors (malignant tumors). Furthermore, the proposed methodology demonstrates efficiency with relatively low execution time, rendering it suitable for real-world applications.

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An integrated deep learning and supervised learning approach for early detection of brain tumor using magnetic resonance imaging

Diagnosing brain tumors is difficult, especially at an early stage of the disease. Conventional approaches often cause delays in providing required treatment to the patients and shorten their lifespan. This paper presents a novel integrated approach with advanced subsets of artificial intelligence, including deep learning and supervised learning algorithms. These new technologies have demonstrated outstanding potential due to their ability to capture the appropriate features based on the input data. They can assist medical experts in identifying abnormal growth of cells inside the brain. We use publicly available brain magnetic resonance imaging (MRI) datasets to diagnose brain tumors and develop an automated system. The proposed approach uses data augmentation to enhance the image sizes and maintain standardization. We then deploy a visual geometry group with 16 layers following transfer learning to help minimize the medical experts’ workload in making accurate decisions. We extract the most significant features and improve the diagnostic speed and accuracy using a supervised learning algorithm and linear support vector machines (SVM). The proposed model outperforms the existing approaches with an accuracy of 98.87%, precision of 99.09%, recall of 98.73%, specificity of 99.02%, and F1-score of 98.91%.

Multiple Disease Prediction Using Machine Learning

In various regions worldwide, the incidence of life-threatening diseases such as brain tumors, cataracts, pneumonia, and malaria remain alarmingly high, posing significant challenges to public health systems. Multiple factors contribute to the onset of these conditions, including genetic predispositions, environmental exposures, and lifestyle choices. This research endeavors to develop and evaluate a data-driven predictive model for early detection of brain tumors, cataracts, pneumonia, and malaria utilizing convolutional neural network (CNN) algorithms trained on medical imaging data. The proposed method integrates diverse patient parameters, including demographic information, medical history, and imaging features, to forecast the risk of developing these diseases. CNN architecture is chosen as the preferred model due to its ability to effectively analyze complex image data. Ethical considerations and privacy concerns regarding the handling of sensitive medical information are thoroughly examined, emphasizing the importance of responsible model development. Furthermore, the interpretability of CNN models is addressed to facilitate understanding among healthcare professionals and patients. The developed predictive system demonstrates promising accuracy and reliability, with CNN achieving notable performance metrics across all disease categories. A web-based platform is implemented to facilitate easy input and disease prediction based on medical images. The dataset utilized in this study is sourced from reputable medical institutions and research organizations, ensuring data quality and integrity. The findings of this research contribute valuable insights into the application of CNN-based predictive models in healthcare, offering a pathway for integrating such systems into clinical practice for early disease diagnosis and intervention

Brain Tumor Detection and Classification Using Deep Learning Models on MRI Scans

INTRODUCTION: The primary goal of artificial intelligence (AI) is to develop computers that exhibit human-like behavior and functionality. Computer-based activities employing artificial intelligence encompass a variety of extra features beyond only pattern detection, planning, and problem resolution. METHODOLOGY: Machines use a set of techniques collectively called "deep learning." Magnetic resonance imaging (MRI) is employed with the use of deep learning methods to develop models that can effectively identify and classify brain cancers. This technique facilitates the rapid and straightforward detection of brain cancers. Brain problems mainly arise from the abnormal multiplication of brain cells, leading to detrimental alterations in brain structure and finally culminating in the development of cancer in the brain, malignant. Early detection of brain tumors along with following effective intervention can reduce mortality rates. This paper proposes convolutional neural network (CNN) architecture to effectively detect brain cancers using magnetic resonance (MR) images. RESULTS: This research further examines several models, including ResNet-50, VGG16, and Inception V3, and compares the proposed architecture and these models. For the efficacy of the models, many measures were evaluated, including accuracy, recall, loss, and area under the curve (AUC). After analyzing several models and comparing them with the suggested model using the specified metrics, it was determined that the proposed model exhibited superior performance compared to the alternative models. Based on an analysis conducted on data from 3265 MR images. CONCLUSION: It was seen that the CNN model exhibited a classification precision of 93.3%. Additionally, the area under the receiver operating characteristic curve (AUC) was determined to be 98.43%, while the recall rate was 91.19%. Furthermore, the model's loss function yielded a value of 0.25. Based on a comparative analysis with other models, it can be inferred that the suggested model is highly reliable in detecting various types of brain cancers at an early stage.

Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics

The early diagnosis of brain tumors is critical in the area of healthcare, owing to the potentially life-threatening repercussions unstable growths within the brain can pose to individuals. The accurate and early diagnosis of brain tumors enables prompt medical intervention. In this context, we have established a new model called MTAP to enable a highly accurate diagnosis of brain tumors. The MTAP model addresses dataset class imbalance by utilizing the ADASYN method, employs a network pruning technique to reduce unnecessary weights and nodes in the neural network, and incorporates Avg-TopK pooling method for enhanced feature extraction. The primary goal of our research is to enhance the accuracy of brain tumor type detection, a critical aspect of medical imaging and diagnostics. The MTAP model introduces a novel classification strategy for brain tumors, leveraging the strength of deep learning methods and novel model refinement techniques. Following comprehensive experimental studies and meticulous design, the MTAP model has achieved a state-of-the-art accuracy of 99.69%. Our findings indicate that the use of deep learning and innovative model refinement techniques shows promise in facilitating the early detection of brain tumors. Analysis of the model’s heat map revealed a notable focus on regions encompassing the parietal and temporal lobes.Graphical Grad-CAM heat map visualization results

Development and validation of a platform based on 3D stochastic sensors for fast screening of whole blood and brain tissue for brain cancer

A novel stochastic platform was assembled by integrating a sensor based on single-wall carbon nanotube paste and ZnO modified with (Z)-N-[2-(4-hydroxyphenyl) ethyl] octadec-9-enamide for the enantioanalysis of two amino acids in whole blood and brain tissue tumor samples. The stochastic platform was utilized to obtain wide concentration ranges for the L-tryptophan, D-tryptophan, L-glutamine, and D-glutamine. The reported sensitivities for both isomers of glutamine exhibited a comparable magnitude order of 107. In contrast, among the four enantiomers, D-Trp displayed the highest sensitivity, measuring at 4.43 × 108. The limits of quantification were in the order of 1.00 × 10−18 mol•L−1. The efficacy of the platform was demonstrated through successful testing on biological samples. The advantages of using this type of platform for such analysis are the possibility of identifying and quantifying in one run different types of biomarkers as well as other substances of biological importance usually requested by medical doctors to be quantified, which can take the test from a fast-screening method to a fast diagnostic method. The analysis is performed within minutes, which is very important for illnesses such as brain cancer that can develop quickly in the body. The early detection of brain tumors can save lives. Furthermore, the portability of the developed detection technology makes it suitable for on-site analysis.

Early detection of brain tumors: Harnessing the power of GRU networks and hybrid dwarf mongoose optimization algorithm

Brain tumor detection is a challenging problem that requires accurate and robust methods to identify and locate the abnormal regions in the brain images. MRI is the most commonly used imaging modality for brain tumor diagnosis, as it can provide high-resolution and contrast images of the brain tissues. However, manual analysis of MRI images by human experts is time-consuming, subjective, and prone to errors. Therefore, there is a need for automated and intelligent methods that can analyze the MRI images and detect brain tumors effectively and efficiently. In this paper, we propose a novel machine learning method that combines the advantages of the Gated Recurrent Unit (GRU) networks and the Enhanced Hybrid Dwarf Mongoose Optimization (EHDMO) algorithm for brain tumor detection. The GRU networks are a type of Recurrent Neural Network (RNN) that can process sequential data, such as natural language or time series. We employ the EHDMO algorithm to fine-tune the parameters of the GRU networks, such as the weight matrices and bias vectors for each gate and the candidate's hidden state, along with the number of hidden units in the network. The proposed method is applied to the brain tumor detection problem using the “Brain-Tumor-Progression” dataset. Results show that the proposed method achieves a sensitivity of 0.98, a specificity of 0.97, a PPV of 0.98, an NPV of 0.98, and an accuracy of 0.95. These results indicate that the proposed method can accurately and robustly detect brain tumors from MRI images. The method also is compared with some of the most recent methods, such as BrainMRNet, VGG19, ASSO, CNN/POA, and YOLOv2. The proposed method outperforms these methods in terms of sensitivity, specificity, PPV, NPV, and accuracy, demonstrating its effectiveness and efficiency.

Brain tumor malignancy classification using improved VGG16 based on MRI images

This study introduces a method based on Magnetic Resonance Imaging (MRI) images and Convolutional Neural Networks (CNN) to categorize the malignancy of brain tumors. The major goal of this research is to develop an accurate and reliable model that aids in tumor early identification, leading to better patient outcomes. Specifically, first, the MRI images undergo preprocessing, which includes intensity normalization and random brightness and contrast adjustments for data augmentation. Next, the Visual Geometry Group (VGG16) model serves as a baseline, enhanced with flatten, dense, and dropout layers. Transfer learning is applied to leverage pre-trained features from VGG16, improving the generalization capability of model. A sizable collection of brain MRI pictures is used to train the model. The experimental outcomes on the MRI dataset illustrate the efficacy of the suggested model. The CNN-based approach achieves high accuracy in classifying brain tumor malignancy based on MRI images, indicating its potential in medical image analysis and its significance in early tumor detection and diagnosis. The proposed model serves as a valuable tool for healthcare professionals, aiding in well-informed decisions and providing an ideal medical approach for early detection of brain tumors. This research opens avenues for future work in medical imaging and deep learning, paving the way for improved treatment and diagnosis of brain tumors.

YOLOv5x-based Brain Tumor Detection for Healthcare Applications

Brain tumors arise from the emergence of abnormal cells in brain tissue and are considered one of the most perilous conditions affecting individuals of all ages, including both children and adults. The disease advances swiftly, and the likelihood of survival diminishes significantly without prompt and adequate treatment. Hence, accurate diagnosis and meticulous treatment planning play a pivotal role in improving the patient's life expectancy. Neurologists and radiologists play a crucial role in the early detection of brain tumors. However, manually identifying and segmenting brain tumors from Magnetic Resonance Imaging (MRI) data poses significant challenges and is susceptible to inaccuracies. The need for an automated brain tumor detection method becomes imperative to achieve early detection of brain tumors.Objective: The objective of the paper is to measure the capability of You Only Look Once version 5 (YOLOv5x) in brain tumor detection in the early stage so that patients can be treated accordingly.Methods: YOLOv5x is examined; Brats (Brain Tumor Segmentation) image and roboflow dataset has been used. Model performance is evaluated using precision, recall rate, F1 score, and Mean Average Precision (mAP).Results: YOLOv5x exhibited precision (98.7), recall (95.6), F1-score (93), mAP at a learning rate of 0.5 (98.4), and the total time taken for implementation of work is 193.20 minutes.Conclusion: YOLOv5x showed improved performance for the detection of brain tumors on dynamic contrast-enhanced MRI when compared with state of art existing work. It is also the fastest and accurate method indicating a greater potential for clinical application.

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

Effective treatment of brain tumours depends on early identification of tumour tissues. Categorizing tumors is essential for its early identification. In order to split and categorise brain tumours using MRI images, various machine learning algorithms are examined. Preprocessing methods including image augmentation, rotation flipping, and adaptive deformation are used on the BRATs dataset to improve the data quality. Convolutional neural network (CNN) feature extraction techniques like VGG19 and InceptionV3 are applied. K-Nearest Neighbours (KNN) and logistic regression techniques are used to categorise the features. The proposed models’ accuracy for Logistic Regression, KNN, VGG19, Inception V3 and VGG19 with augmentation is 84.66%, 90.68%, 92.17%, 91.56% and 95.43% respectively. These findings imply that CNN-based feature extraction and picture augmentation techniques greatly improves the accuracy of brain tumour identification, Overall thecInception V3 with augmentation produces the best results.

Enhanced brain tumor classification using graph convolutional neural network architecture

The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26 layered CNN that takes in a Graph input pre-convolved using Graph Convolution operation. The objective of Graph Convolution is to modify the node features (data linked to each node) by combining information from nearby nodes. A standard pre-computed Adjacency matrix is used, and the input graphs were updated as the averaged sum of local neighbor nodes, which carry the regional information about the tumor. These modified graphs are given as the input matrices to a standard 26 layered CNN with Batch Normalization and Dropout layers intact. Five different networks namely Net-0, Net-1, Net-2, Net-3 and Net-4 are proposed, and it is found that Net-2 outperformed the other networks namely Net-0, Net-1, Net-3 and Net-4. The highest accuracy achieved was 95.01% by Net-2. With its current effectiveness, the model we propose represents a critical alternative for the statistical detection of brain tumors in patients who are suspected of having one.

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor.

The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods.

An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble.

Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approach.