A study of classification and feature extraction techniques for brain tumor detection

Detection and classification of brain tumors are prominent tasks in neuroimaging, as it ensures accurate and timely diagnosis for effective treatment planning. This study explores the utility of machine learning models, such as convolutional neural networks, VGG 16 and 19 architectures, and recurrent neural networks, in improving the detection of brain tumor anomaly in both CT and MRI. A total of 3400 images were integrated and collected from multiple sources and prepared for usage by conducting meticulous preprocessing and feature extraction. Following the preparation process, the models were trained using stratified dataset split with a 70/30 ratio. The testing results indicated that the VGG 16 and VGG 19 architectures yielded the highest performance results, as they produced the optimal precision, recall, and F1-score values that reached up to 96.7%, 97.2%, and 96.5%, respectively, along with the highest AUC-ROC scores. In comparison, the CNN and RNN models presented lower performance results in each measured metric. The superiority of the VGG architectures strengthes the idea that the complexity and the capacity of the model to memorize and retain the imaging features is crucial for the accurate detection of the tumors. As such, the improved results can be helpful for healthcare professionals, as they provide powerful tools to ensure precise detection and characteristics of brain tumors. Nevertheless, further research is required to validate the results with a larger dataset, and the adherence of the study to the black-box nature of the model poses limitations on the interpretation. In any case, the study supplements the growing body of research in medical imaging and contributes to the prevention and management of brain tumors.

Medical imaging plays an important role to generate images of mankind for clinical and medical research. In this paper, we are focused on the detection of brain tumor using MRI images, one of the modalities of medical imaging. Basically, tumor is an abandoned growth of tissues in any portion of the human body. Nowadays, automatic detection of brain tumor is the foremost area for research. In this paper, we proposed a system that checks whether the tumor is present or not; if the tumor is present, then classify the tumor. For detection and classification of brain tumor, we have done an experiment on 150 T1-weighted MRI brain images. The supervised classification has applied for classification so that training set is created using texture feature that is extracted with GLCM and DWT methods; for feature selection, principle component analysis has been used. The experimental results show KSVM gives 97% accuracy to classify the brain tumor. We have also calculated the area and volume of the tumor to find stages of the tumor.

Automatic identification of brain tumors using MRI images is a significant process for avoiding earlier death. A brain tumor is an unrestrained growth of cancerouscells in the brain. The process of finding the tumor margins from healthy cells is still a difficult task due to the inconsistency and complexity of the location, size, shape, and texture of these lesions. Some approaches make use of automatic detection of brain tumors. However, the time and accurate detection of the brain tumors from the multi- spectral MRI scans mages are challenging issue. A noveltechnique called Hermitian Multi-wavelet Kriging Regressive Feature Transformation based Brain Tumor Detection (HMKRFT-BTD) technique is presented for precise mind cancer discovery with least time utilization. The segmented image is given as input to the HMKRFT-BTD technique to perform the brain tumor detection. Initially, the Hermitian Multi-wavelet transform is applied to decompose the image into a number of sub-bands. For each sub-band, the texture, color, shape, intensity features of the image are extracted to detect the tumor for minimizing the time consumption. Finally, Kriging Regression is applied to analyze the extracted features with the testing disease features. Based on the analysis, normal or tumor malignancy is correctly identified with higher accuracy and lesser time. Experimental evaluation is carried out on factors such as tumor detection accuracy, false- positive rate, and tumor detection time with respect to anumber of MRI images. The qualitative and quantitative results indicate that the HMKRFT-BTD technique is more effective to achieve a higher tumor detection accuracy and lesser false positive rate as well as detection time when compared to the current cutting-edge methods.

Brain tumors are a group of diseases characterized by abnormal growths of cells in the brain that can cause severe neurological symptoms. In recent years, the advent of artificial intelligence (AI) techniques has shown great promise in enhancing brain tumor detection. This survey research discusses methods and techniques used for AI-based brain tumor detection. Brain tumors pose significant health risks, necessitating accurate and timely detection for effective treatment. The study defines brain tumors and emphasizes the need for precise detection methods due to tumor variations. Biomarkers associated with brain tumors are investigated, highlighting their potential as diagnostic and prognostic indicators. The utilization of deep learning (DL) models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and 3D CNNs, is examined, providing a comparative analysis of their strengths and limitations. The importance of datasets, such as TCIA, BRATS, and ISLES, is discussed in training and evaluating AI models for brain tumor detection. This survey aims to contribute to the understanding and progress of AI-based brain tumor detection along with the comparison of some deep learning models, providing insights for researchers and healthcare professionals working towards improving patient outcomes. Keywords: Brain tumor; Artificial Intelligence; Tumor detection; Diagnosis; Image Preprocessing; Deep Learning; Neural Networks, Molecular biomarkers.

The diagnosis of cancer can be challenging and time-consuming due to the complex characteristics of tumors and inherent noise in medical imaging. The significance of early detection and localization of tumors must be considered. Radiological imaging techniques can detect and potentially forecast the presence of neoplastic growths at various phases. The expeditiousness of the diagnosis process can be notably enhanced by amalgamating these images with algorithms designed for segmentation and relegation. Early detection of tumors and accurate localization of their position are critical factors. Medical scans, when used with segmentation and relegation procedures, enable the prompt and precise detection of cancerous tumor regions. The identification of malignant tumors enables this achievement. The present article introduces a framework for detecting brain tumors based on a convolutional neural network (CNN). The initial step in processing brain magnetic resonance imaging (MRI) images involves the application of a Gaussian filter to eliminate any noise present. Subsequently, CNN and long short-term memory (LSTM) deep learning methodologies are employed to classify images. CNN has demonstrated improved accuracy in the classification and detection of brain tumors. CNN has achieved an accuracy of 99.25% in cancer image classification. The sensitivity and specificity of CNN are also 98.75% and 99.25%, respectively.

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.

In the human body, the most important and the complex organs work with billions of cells in the brain. The abnormal growth or uncontrolled division of cells around the brain will cause a brain tumor. These group of cells which affect the functioning of the brain and also destroys the human cells. In the olden days, the detection of brain tumors is way much harder than nowadays. The usage of modern computer vision techniques has made the detection to be more accurate and easy. In this paper, firstly the detection of tumor in the brain was performed using a Sequential Neural Network (SNN) model which classifies the symptoms, as the brain tumor and then Magnetic Resonance Images (MRI) Scans are used for the further confirmation. The SNN model has an accuracy of 99.36% whereas the Convolutional Neural Network (CNN) Model used in this paper is 99.89% accurate.

Accurate brain tumor detection and localization are essential for timely diagnosis and treatment in medical imaging. In this paper, we propose an advanced deep learning model using Convolutional Neural Networks (CNN) integrated with ResNet-101 for brain tumor detection, tumor localization, and classification of missing modality images. The model leverages the residual learning capabilities of ResNet-101 for effective feature extraction, enhancing the detection of brain tumors even in incomplete or missing modality. Additionally, a localization module is incorporated to pinpoint the tumor region, improving interpretability for clinical use. Results demonstrate that the CNN-ResNet-101 model excels in tumor detection and classification, maintaining high accuracy across both complete and missing modality images. Furthermore, the model's ability to localize tumors without compromising classification performance is highlighted through improved precision and recall scores. The proposed model achieves better performance for robust brain tumor detection and localization in challenging medical imaging conditions. The proposed model is evaluated and benchmarked against other techniques, with a comprehensive performance comparison using key metrics such as accuracy, precision, recall, and F1-score.

A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of highquality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co- occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GASVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software.

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images, which helps solve the difficulties of brain tumor identification efficiently and accurately. Our methodology is based on the accurate and efficient selection of tumorous areas. That reduces computational complexity and time. We have validated the designed experimental setup on a standard dataset, BraTS 2020. We used binary evaluation matrices based on Dice Similarity Coefficient (DSC) and Mean Average Precision (mAP). The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method. The suggested approach attained an average DSC of 0.92 and mAP 0.92 for 10 patients, while on the whole dataset, the scores are DSC 0.89 and mAP 0.90. The following results clearly show the performance efficiency of the proposed methodology.

This study explores the potential of the VGG-16 architecture, a Convolutional Neural Network (CNN) model, for accurate brain tumor detection through deep learning. Utilizing a dataset consisting of 1655 brain MRI images with tumors and 1598 images with- out tumors, the VGG-16 model was fine-tuned and trained on this data. Initial training achieved an accuracy of 91%, which was improved to 94% after hyperparameter optimization. The model’s sensitivity, specificity, precision, recall, and F1 scores were strong, indicating its potential in accurately detecting brain tumors. The performance of the VGG-16 model was compared to several other techniques for brain tumor detection, including EasyDL, GoogLeNet, GrayNet, ImageNet, CNN, and a Multivariable Regression and Neural Network model. Although it did not achieve the highest accuracy, it outperformed GoogLeNet and ImageNet and demon- strated comparable accuracy to GrayNet and the Multivariable Regression and Neural Network. Its sensitivity and specificity suggest its potential in identifying tumors that other methods might miss, reinforcing its potential usefulness in medical applications. Never- theless, there is room for improvement. Future studies could collect and annotate larger datasets to improve model generalizability. Exploring other deep learning architectures and enhancing model interpretability could further boost its clinical relevance. Despite these challenges, this study demonstrates the untapped potential of the VGG-16 architecture in brain tumor detection and contributes to the growing body of research on applying deep learning in the medical field.

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: Accurate and early detection of brain tumors from MRI images plays a vital role in improving prognosis and treatment planning for patients.However, manual interpretation of MRI images is time-consuming and prone to human error.This study aims to present and evaluate an automated and efficient method based on the YOLOv8 algorithm and deep learning for detecting brain tumors in MRI images.Materials and Methods: In this study, the YOLOv8 model was trained using a dataset of 500 labeled brain tumor MRI images.The model's performance on the evaluation and test datasets was assessed using the metrics of Precision, Recall, and AP50.The model was trained in the Google Colab environment using a Tesla T4 GPU and over 100 epochs.Results: The results showed that the trained YOLOv8 algorithm achieved high accuracy and speed in detecting brain tumors.The obtained AP50 values on the evaluation (94.5) and test (94.6)datasets, along with high Precision and Recall values, were evidence of the model's strong and stable performance in tumor detection.The qualitative examination of sample images also confirmed the high accuracy of the algorithm in locating and detecting tumors.Furthermore, the short training time and high detection speed were other advantages of the YOLOv8 algorithm in this study. Conclusion:The present study demonstrates that the YOLOv8 algorithm has significant potential for the automatic and efficient detection of brain tumors in MRI images.The appropriate balance between accuracy and speed, as well as the good generalizability of the model, makes YOLOv8 a valuable auxiliary tool for radiologists achieving faster and more accurate detection of brain tumors.Future studies focus on increasing the volume of training data, improving the model architecture, and clinically evaluating the proposed approach.

This study introduces a novel Black Widow‐Optimized Dense CNN (BW‐DCNN) framework for the automated detection and segmentation of brain tumors in MRI scans, aiming to enhance early diagnosis and treatment planning. With the increasing frequency of brain tumors, early and accurate detection becomes crucial for effective treatment. Magnetic Resonance Imaging (MRI) is a diagnostic technique owing to its excellent sensitivity. However, manual MRI data analysis is time‐consuming and prone to errors. Leveraging advancements in Convolutional Neural Networks (CNNs) and optimization algorithms, the proposed BW‐DCNN framework utilizes a unique integration of preprocessing, segmentation enhancement, and classification techniques optimized through Black Widow Optimization to improve diagnostic accuracy and efficiency. Evaluating the BW‐DCNN framework against existing methodologies, including DCNN, DNN, and DBN, demonstrates superior performance across a comprehensive suite of metrics. These results highlight the potential of the BW‐DCNN approach to significantly advance the capabilities of computer‐aided diagnostic systems in medical imaging, offering a promising direction for future research and application in clinical settings.