Identify Brain Tumors Research Articles

A hybrid EfficientNet-DbneAlexnet for brain tumor detection using MRI images

The rapid growth of abnormal cells in the brain presents a serious risk to the health of humans as it can result in death. Since these tumors have a varied range of shapes, sizes, and positions, identifying Brain Tumors (BTs) is challenging. Magnetic Resonance Images (MRI) are most utilized for identifying malignant tumors. This paper develops a new approach, named EfficientNet-Deep batch normalized eLUAlexnet (EfficientNet-DbneAlexnet) for detecting BTs. Firstly, the input MRI image is transmitted for image enhancement. Here, the image is enhanced by the Piecewise Linear Transformation (PLT). After this, skull stripping is carried out, which is performed by the Fuzzy Local Information C Means (FLICM). Following this, the tumor area in the image is segmented with the help of a Projective Adversarial Network (PAN). The segmented image is later applied to the feature extraction module, wherein features like textural and statistical features are extracted. Finally, the BT detection is accomplished using the developed EfficientNet-DbneAlexnet, which is created by assimilating EfficientNet and Deep batch normalized eLUAlexnet (DbneAlexnet). The results demonstrate that EfficientNet-DbneAlexnet obtained a sensitivity of 90.36 %, accuracy of 92.77 %, and specificity of 91.82 %.

Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning

Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together to train detection models without jeopardizing patient privacy. This paper presents an approach called federated learning that may be used to improve brain tumor identification while protecting patient privacy. Brain tumors are dangerous medical disorders that need to be accurately diagnosed in order to be effectively treated. However, sharing private patient information is a common practice in traditional medical data analysis methodologies, which raises privacy issues. Federated learning helps with this by enabling cooperative training of a common model amongst several hospitals or institutions without requiring the exchange of raw data. This method protects patient privacy by having each institution train the model using its own local data and only sharing model updates. We show through trials that our method is efficient in reliably identifying brain tumors while upholding privacy norms, presenting a viable option for improving medical diagnosis without jeopardizing patient privacy.

QOL-28. CANCER-RELATED DISTRESS IN NEURO-ONCOLOGY: PATIENTS INFORM PROGRAM DEVELOPMENT FOR ENHANCING QUALITY OF LIFE

Abstract BACKGROUND Brain tumor diagnosis and treatment result in significant negative sequelae, including cognitive decline, fatigue, and psychosocial symptoms. This study aimed to identify brain tumor patients’ sources of cancer-related distress and examine their interest in various treatment modalities targeting cognitive decline to inform program development for this underserved population. METHODS In pursuit of program development, patients with both primary and secondary brain tumors completed an anonymous survey. They reported demographics, tumor characteristics, subjective cognitive functioning; and, rated sources of cancer-related symptom distress (i.e., cognitive changes, fatigue, physical symptoms, motor functioning, balance, mood, and relationship) and interest in treatments targeting cancer-related cognitive decline (i.e., cognitive rehabilitation, mindfulness-based practices, psychotherapy, and medication). Descriptive statistics, t-tests, chi-square, and linear regressions were conducted. RESULTS Among participants (N=137, Mage=49.6±15.8, 58.1% female, 84.8% White), 50.4% endorsed subjective cognitive functioning below the mean (MFACT-Br-CI=22.6±7.5). Nearly half reported significant distress due to cancer-related fatigue (49.6%) and cognitive change (48.9%). Patients’ mean severity of distress due to cognitive decline was significantly greater relative to all other cancer-related symptoms (ps<.001), except fatigue (p>.05). Age, gender, or time since diagnosis were not significant predictors (ps>.05) of distress due to cognitive decline. Most patients with distress due to cognitive change were interested in cognitive rehabilitation (63.6%) and mindfulness-based practices (61.6%), and only 40.4% were interested in medication. Interest in cognitive rehabilitation was not predicted by symptom distress, subjective cognitive functioning, age, gender, or time since diagnosis (ps>.05). Gender was the only significant predictor of interest in mindful movement (b=.18, p=.041), with greater interest among women (χ2(1)=4.65, p=.031). CONCLUSIONS Fatigue and cognitive decline were the most distressing cancer-related symptoms for neuro-oncology patients. These findings emphasize the need for integrated interventions, including cognitive rehabilitation and mindfulness, as well as multi-targeted and individualized treatments for fatigue and cognitive decline in neuro-oncology.

Highly accurate brain tumor detection with high sensitivity using transform-based functions and machine learning algorithms.

Brain tumor is an extremely dangerous disease with a very high mortality rate worldwide. Detecting brain tumors accurately is crucial due to the varying appearance of tumor cells and the dimensional irregularities in their growth. This poses a significant challenge for detection algorithms. Currently, there are numerous algorithms utilized for this purpose, ranging from transform-based methods to those rooted in machine learning techniques. These algorithms aim to enhance the accuracy of detection despite the complexities involved in identifying brain tumor cells. The major limitation of these algorithms is the mapping of extracted features of a brain tumor in the classification algorithms. To employ a combination of transform methods to extract texture feature from brain tumor images. This paper employs a combination of transform methods based on sub band decomposition for texture feature extraction from MRI scans, hybrid feature optimization methods using firefly and glow-worm algorithms for selection of feature, employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods. The algorithm under consideration has been put into practice using MATLAB, utilizing datasets from BRATS (Brain Tumor Segmentation) for the years 2013, 2015, and 2018. These datasets serve as the foundation for testing and validating the algorithm's performance across different time periods, providing a comprehensive assessment of its effectiveness in detecting brain tumors. The proposed algorithm achieves maximum detection accuracy, detection sensitivity and specificity up to 98%, 99% and 99.5% respectively. The experimental outcomes showcase the efficiency of the algorithm in detection of brain tumor. The proposed work mainly contributes in brain tumor detection in the following aspects: a) use of combination of transform methods for texture feature extraction from MRI scans b) hybrid feature selection methods using firefly and glow-worm optimization algorithms for selection of feature c) employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods.

Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network.

Brain tumors are a leading cause of death globally, with numerous types varying in malignancy, and only 12% of adults diagnosed with brain cancer survive beyond five years. This research introduces a hyperparametric convolutional neural network (CNN) model to identify brain tumors, with significant practical implications. By fine-tuning the hyperparameters of the CNN model, we optimize feature extraction and systematically reduce model complexity, thereby enhancing the accuracy of brain tumor diagnosis. The critical hyperparameters include batch size, layer counts, learning rate, activation functions, pooling strategies, padding, and filter size. The hyperparameter-tuned CNN model was trained on three different brain MRI datasets available at Kaggle, producing outstanding performance scores, with an average value of 97% for accuracy, precision, recall, and F1-score. Our optimized model is effective, as demonstrated by our methodical comparisons with state-of-the-art approaches. Our hyperparameter modifications enhanced the model performance and strengthened its capacity for generalization, giving medical practitioners a more accurate and effective tool for making crucial judgments regarding brain tumor diagnosis. Our model is a significant step in the right direction toward trustworthy and accurate medical diagnosis, with practical implications for improving patient outcomes.

Brain Tumor Detection using Hybrid CNN in fMRI images

One of the deadliest brain tumors in the world. Both adults and children are at risk for the disease. Depending on their position, appearance and shape, it has a low survival rate and is available in different varieties. Incorrect classification of the brain tumor will have negative consequences. Early detection of the tumor type and stage is necessary for selecting a specific treatment. Identifying brain tumors can be done effectively by examining fMRI, or functional magnetic resonance imaging. Due to the various information and various types of tumors in brain, the manual process takes lot of time to identify, and there may be chances of human error. Therefore, it is necessary to have an intelligent diagnostic system. The recent evolution of image categorization systems has been influenced by the use of deep convolution neural networks (CNN). In this research work, a new hybrid paradigm with images combines a convolution neural network (CNN) and a masked autoencoder for distribution estimation (MADE). Three different forms of brain tumors were tested using T1-weighted contrast enhanced images. The CNN-MADE hybrid's excellent classification performance is demonstrated by the results, despite the limited availability of medical images. A new layered model was proposed to categorize brain tumors through MRI. The proposed approach is more successful than the alternatives based on the experiments' results.

Advancements in brain tumor identification: Integrating synthetic GANs with federated-CNNs in medical imaging analysis

Brain tumors are a significant health concern worldwide, necessitating accurate and timely diagnosis for effective treatment planning and management. However, conventional methods for brain tumor identification through medical imaging analysis often face challenges related to accuracy, efficiency, and privacy concerns. Current approaches may struggle with limited datasets, privacy regulations hindering data sharing, and the need for specialized expertise in interpreting medical images. Accurately identifying brain tumors is pivotal in diagnosis, treatment planning, and patient prognosis. This research proposes a novel approach for advancing brain tumor identification by integrating Synthetic Generative Adversarial Networks with federated convolutional neural Networks in medical imaging analysis. Federated-CNNs are a type of neural network architecture designed for federated learning scenarios. In federated learning, model training occurs locally on data distributed across multiple devices or institutions without exchanging raw data. Federated CNNs allow collaborative model training across these distributed datasets by aggregating local model updates rather than exchanging raw data. This approach ensures that sensitive data remain localized within each participating institution, thus addressing privacy concerns in medical imaging analysis. Our methodology harnesses the power of GANs to generate synthetic brain MRI images, addressing data scarcity issues commonly encountered in medical imaging datasets. These synthetic images are then utilized in conjunction with Federated-CNNs, enabling cooperative model training between many healthcare institutions while maintaining the anonymity and privacy of data. Moreover, integrating Federated CNNs ensures that sensitive medical imaging data remain localized within participating institutions, addressing data privacy concerns and fostering collaboration among medical professionals. The research advances medical imaging analysis by introducing a novel methodology that leverages existing technologies to improve brain tumor identification accuracy. Specifically, the feature extraction phase using DenseNet121, implemented in MATLAB, achieves an outstanding accuracy of 99.82 % and outperforms Various existing methods, including Inception-V3, ResNet-18, and GoogleNet, demonstrating the efficacy of our approach in capturing discriminative features from medical imaging data. This high accuracy underscores the potential of our methodology to enhance diagnostic accuracy and clinical decision-making in neurology and oncology. The research offers a promising avenue for further exploration and innovation in medical imaging analysis, with significant implications for improving patient outcomes and advancing healthcare practices.

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.

Design of Terahertz Refractive Index-Based Spiral Hollow-Core Photonic Crystal Biosensor Using Enhanced Probabilistic Pyramid Neural Networks for Brain Tumor Detection

Cancer diagnosis is difficult and costly due to the complexity of the brain. Photonic technology-based biosensors show potential for identifying malignant tissues, including brain tumors, but they are often costly, time-consuming, and computationally difficult. To address these challenges, we propose an enhanced probabilistic pyramid neural networks (EPPNN)-based hollow-core photonic crystal fiber (PCF) biosensor with terahertz refractive index (THzBio-ECPPN) for detection of cancerous brain tumors. The approach is divided into two stages: biosensor design and brain tumor detection. Initially, PCF geometry with suspended cladding and a spiral-shaped hollow-core in the terahertz (THz) band is proposed. The PCF biosensors’ characteristics are then calculated using the EPPNN model. The EPPNN model’s hyperparameters are modified using the circle-inspired optimization algorithm to maximize accuracy and minimize effective mode loss. The proposed biosensor is then used to identify brain tumors. Experimental evaluations utilizing MATLAB show that the suggested strategy surpasses earlier methods, with a higher sensitivity (98%). The sensor has exceptional performance characteristics, such as a high figure of merit of 1.25–1.35 RI range and sensitivity of 50000 nm RIU−1, indicating its potential for precise detection of changes in refractive index. This combination of photonic crystal structures and neural networks has enormous potential for improving cancerous tumor accuracy to 99.92%, precision to 99.23%, specificity to 99.73%,and sensitivity to 99.36% of brain tumor diagnosis.

Enhancing Brain Tumor Classification in MRI: Leveraging Deep Convolutional Neural Networks for Improved Accuracy

Brain tumors are among the deadliest forms of cancer, and there is a significant death rate in patients. Identifying and classifying brain tumors are critical steps in understanding their functioning. The best way to treat a brain tumor depends on its type, size, and location. In the modern era, Radiologists utilize Brain tumor locations that can be determined using magnetic resonance imaging (MRI). However, manual tests and MRI examinations are time-consuming and require skills. In addition, misdiagnosis of tumors can lead to inappropriate medical therapy, which could reduce their chances of living. As technology advances in Deep Learning (DL), Computer Assisted Diagnosis (CAD) as well as Machine Learning (ML) technique has been developed to aid in the detection of brain tumors, radiologists can now more accurately identify brain tumors. This paper proposes an MRI image classification using a VGG16 model to make a deep convolutional neural network (DCNN) architecture. The proposed model was evaluated with two sets of brain MRI data from Kaggle. Considering both datasets during the training at Google Colab, the proposed method achieved significant performance with a maximum overall accuracy of 96.67% and 97.67%, respectively. The proposed model was reported to have worked well during the training period and been highly accurate. The proposed model's performance criteria go beyond existing techniques.

Enhancing Tumor Localization in Brain MRI Images Using a Scalable Systematic Vector Algorithm for Result-Oriented Accuracy

This paper introduces a novel foundational architecture incorporating two primary algorithms. The Partially Enhanced Linear Mean Analysis Algorithm for significant vectorization and the Vertically Enhanced Tensor-Load Interface Algorithm for structural vectorization. These algorithms are meticulously designed to optimize the extraction of valuable information from complex data sets, enabling efficient and insightful historical data analysis. The performance of these algorithms has been rigorously compared with existing significant vectorization and structural methods, demonstrating superior accuracy in mean value estimation. Simulated results provide robust evidence of the high-dimensional capability and throughput of the proposed architecture. Incorporating tools such as Microsoft Excel for data organization, MATLAB for advanced computational analysis, and Python for algorithmic implementation and automation further enhances the framework’s efficiency and precision. Imminent work on this architecture could expand its applicability into multi-functional systems, potentially integrating a multi-role tape framework. Such an expansion would be particularly beneficial for identifying brain tumors with greater accuracy and precision, contributing significantly to medical diagnostics. The proposed system represents a promising advancement in applying computational tools for complex data analysis and structural vectorization.

Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased

<p>Brain tumor is an uncontrolled growth of abnormal cell in the brain. Early diagnosis of brain tumor has a crucial step in this type of cancer, which is fatal. Magnetic resonance imaging (MRI) is one of the examination tools to examine brain anatomy in clinical practice. The high resolution and clear separation of the tissue enable medical experts to identify brain tumor. The earlier of brain tumor is detected, the wider of treatment options. However, manually analysed of brain anatomy on MRI images are time-consuming. Computer-aided diagnosis with automated way is helpful solution to help management with unreliable degrees of automation to trace various tissue boundaries. This study proposes convolutional neural network (CNN) with its excellences to automated features extraction in convolution layer. The popular architectures of CNN, i.e., visual geometry group16 (VGG16), residual network-50 (resNet-50), inceptionV3, mobileNet, and efficientNetB7 in medical image processing are compared to brain tumor classification task. As the results, VGG16 outperformed other architectures of CNN in this study. VGG16 yields 100% accuracy, precision, sensitivity, specificity, and F1-score for testing set data. The results show the excellent performance in classifying brain tumor and no tumor from MRI images that demonstrate the efficiency of system suggested.</p>

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.

Refining neural network algorithms for accurate brain tumor classification in MRI imagery

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model’s effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model’s decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.

SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation

Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However, the downsampling blocks of most existing methods are typically used only for processing the variation in image sizes and lack sufficient capacity for further extraction features. We, therefore, propose SARFNet, a method based on UNet architecture, which consists of the proposed SLiRF module and advanced AAM module. The SLiRF downsampling module can extract feature information and prevent the loss of important information while reducing the image size. The AAM block, incorporated into the bottleneck layer, captures more contextual information. The Channel Attention Module (CAM) is introduced into skip connections to enhance the connections between channel features to improve accuracy and produce better feature expression. Ultimately, deep supervision is utilized in the decoder layer to avoid vanishing gradients and generate better feature representations. Many experiments were performed to validate the effectiveness of our model on the BraTS2018 dataset. SARFNet achieved Dice coefficient scores of 90.40, 85.54, and 82.15 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves state-of-the-art performance compared with twelve or more benchmarks.

Voting Based VGG-16 for Feature Selection and Classification of The Brain Tumor Types

Brain tumors, especially malignant ones, can be life-threatening if not detected and treated early. Identifying benign tumors, such as Meningiomas or Pituitary tumors, enables preventive measures and long-term monitoring to detect any changes in tumor size or behavior. This ensures timely intervention if needed. ML related methods for analyzing types of brain tumors have made significant advancements but brain tumour records often have a mismatch of classes, which means that some kinds of tumours are much less common than others. This may result in models that are biased and work well for the majority of the class but not so well for the minority class. Latest research techniques have focused on deep learning approaches. Identifying brain tumor types using neural networks is a complex but promising approach in medical image analysis. Combining NN with ML techniques is what the suggested model does to get to the exact type of brain tumour. In this regard, the proposed model has extracted the features from the tuned model of VGG-16 and after extracting the features from the network to further classify the stage, the model has applied ensemble voting mechanism.

Microscope-integrated optical coherence tomography for in vivo human brain tumor detection with artificial intelligence.

It has been shown that optical coherence tomography (OCT) can identify brain tumor tissue and potentially be used for intraoperative margin diagnostics. However, there is limited evidence on its use in human in vivo settings, particularly in terms of its applicability and accuracy of residual brain tumor detection (RTD). For this reason, a microscope-integrated OCT system was examined to determine in vivo feasibility of RTD after resection with automated scan analysis. Healthy and diseased brain was 3D scanned at the resection edge in 18 brain tumor patients and investigated for its informative value in regard to intraoperative tissue classification. Biopsies were taken at these locations and labeled by a neuropathologist for further analysis as ground truth. Optical OCT properties were obtained, compared, and used for separation with machine learning. In addition, two artificial intelligence-assisted methods were utilized for scan classification, and all approaches were examined for RTD accuracy and compared to standard techniques. In vivo OCT tissue scanning was feasible and easily integrable into the surgical workflow. Measured backscattered light signal intensity, signal attenuation, and signal homogeneity were significantly distinctive in the comparison of scanned white matter to increasing levels of scanned tumor infiltration (p < 0.001) and achieved high values of accuracy (85%) for the detection of diseased brain in the tumor margin with support vector machine separation. A neuronal network approach achieved 82% accuracy and an autoencoder approach 85% accuracy in the detection of diseased brain in the tumor margin. Differentiating cortical gray matter from tumor tissue was not technically feasible in vivo. In vivo OCT scanning of the human brain has been shown to contain significant value for intraoperative RTD, supporting what has previously been discussed for ex vivo OCT brain tumor scanning, with the perspective of complementing current intraoperative methods for this purpose, especially when deciding to withdraw from further resection toward the end of the surgery.

Comparison of Ultrasonographic Images of Glioblastoma Tumor with Magnetic Resonance Images: Rat Animal Model

Purpose: Magnetic Resonance Imaging (MRI) can guide the surgical strategy to identify brain tumors and monitor treatment response. It is possible to use transcranial Ultrasound (US) for periodical follow-ups. Ultrasound waves pass through the delicate areas of the skull called acoustic windows. In this study, the efficiency of ultrasound imaging was performed to diagnose glioblastoma brain tumors and the results were compared with MR images.&#x0D; Materials and Methods: Male Wistar rats were anesthetized by intraperitoneal injection of Ketamine and Xylazine. A stereotaxic device was used to determine the injection coordinates. C6 GBM cell lines were injected into the brains of rats. After two weeks, the formation of a glioblastoma tumor was confirmed histopathologically. The brain of animals was imaged by B-mode ultrasound and MRI. The section with the largest tumor dimensions was selected and the dimensions of the skull and tumor were measured based on the pixel size of each of the imaging methods. Pearson coefficient of correlation and Limits Of Agreement (LOA) were calculated for comparisons of the skull and tumor dimensions.&#x0D; Results: The skull and the tumor dimensions showed a significant correlation between the B-mode ultrasound and the MRI measurements (R = 0.99 and p &lt; 0.05). According to the Bland-Altman analysis, the mean difference was 0.31 mm (SD = 0.20) for skull and tumor dimensions. The exact shape of the tumor is not completely clear in the ultrasound images, but it can be useful to detect the presence of the tumor and its approximate dimensions.&#x0D; Conclusion: In conclusion, a glioblastoma tumor was produced in the male Wistar rat. The tumor dimensions were properly assessed by B-mode ultrasound image processing and compared with MR imaging.

Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks

Background and ObjectiveThe central organ of the human nervous system is the brain, which receives and sends stimuli to the various parts of the body to engage in daily activities. Uncontrolled growth of brain cells can result in tumors which affect the normal functions of healthy brain cells. An automatic reliable technique for detecting tumors is imperative to assist medical practitioners in the timely diagnosis of patients. Although machine learning models are being used, with minimal data availability to train, development of low-order based models integrated with machine learning are a tool for reliable detection. MethodsIn this study, we focus on comparing a low-order model such as proper orthogonal decomposition (POD) coupled with convolutional neural network (CNN) on 2D images from magnetic resonance imaging (MRI) scans to effectively identify brain tumors. The explainability of the coupled POD-CNN prediction output as well as the state-of-the-art pre-trained transfer learning models such as MobileNetV2, Inception-v3, ResNet101, and VGG-19 were explored. ResultsThe results showed that CNN predicted tumors with an accuracy of 99.21% whereas POD-CNN performed better with about 1/3rd of computational time at an accuracy of 95.88%. Explainable AI with SHAP showed MobileNetV2 has better prediction in identifying the tumor boundaries. ConclusionsIntegration of POD with CNN is carried for the first time to detect brain tumor detection with minimal MRI scan data. This study facilitates low-model approaches in machine learning to improve the accuracy and performance of tumor detection.

Detection and Classification of Brain Tumor using Deep Learning Techniques

Abstract: Brain tumors are abnormal enlargements of nerves that disrupt normal brain function and can cause death. Locating tumor-affected brain cells can be time-consuming. Early detection and appropriate treatment are critical for saving lives. However, conventional methods for identifying brain tumors through image processing have limitations in both accuracy and processing speed. In this study, we propose using the EfficientNet architecture, a cutting-edge convolutional neural network (CNN), to improve the accuracy and efficiency of brain tumor detection. We present a comprehensive approach that includes preprocessing steps to prepare the input data and data augmentation techniques to enhance the dataset, thus improving the CNN model's generalization and robustness.