Hermitian Multi-Wavelet Kriging Regressive Feature Transformation for Tumor Detection

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.

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.

Medical imaging aids in the analysis of interior parts of the human body such as the functioning of the organs or tissues for early treatment of diseases. Many different types of medical imaging technologies exist, for example, X-ray radiography, magnetic resonance imaging, endoscopy, positron emission tomography, CT scan (computed tomography), and many more. A tumor is an abnormal tissue in the brain which causes damage to the functioning of the cell. Therefore, brain tumor detection is an incredibly tricky task. Manual detection of a tumor is quite risky as it involves the insertion of a needle in the brain. Thus, there is a need for automated brain tumor detection systems. The well-timed detection of the tumor can add to accurate treatment and can increase the survival rate of patients. From machine learning techniques, namely K-nearest neighbor, support vector machine, and more to soft computing techniques, namely artificial neural network, self-organizing map, and others hold a significant stand in detection and categorization of brain tumor. Various methods including deep learning-based classifiers such as convolutional neural network, recurrent neural network, deep belief network (DBN), and others are used to make it easier to detect the tumor. Hybrid classifiers were also used for classification systems such as combining the machine learning approach with soft computing. This study is to summarize and compare the work of various authors on automatic brain tumor detection using medical imaging. Based on the accuracy, specificity, and sensitivity parameters, the results of different techniques are analyzed and compared graphically.

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.

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.

Early and accurate detection of brain tumors in Magnetic Resonance Imaging (MRI) scans is essential for effective treatment and improved patient survival. Although MRI is a widely used diagnostic tool, the manual interpretation of these images is often time-consuming and prone to variability among medical professionals. In biomedical imaging, deep learning methods have been increasingly explored to address these challenges, particularly for automated tumor detection and segmentation. This study compares three advanced object detection models—YOLO-v9, YOLO-v10, and YOLO-v11—evaluated on a dedicated brain tumor MRI dataset. Performance was assessed using multiple metrics, including precision, recall, F1-score, Intersection over Union (IoU), and mean Average Precision (mAP) at 0.5:0.95. In addition, Non-Maximum Suppression (NMS) and frames-per-second (FPS) rates were examined to determine efficiency and feasibility for real-time inference. The results demonstrate that all three YOLO-based architectures achieve high accuracy and efficiency, highlighting their suitability for medical image analysis. Among them, YOLO-v10 achieved the best balance of performance, recording the highest precision (0.95), a substantial recall value (0.88), and superior mAP scores. These outcomes confirm YOLO-v10’s advantage in providing reliable and consistent detection compared to YOLO-v9 and YOLO-v11. Overall, the findings underscore the potential of advanced YOLO models in supporting clinical decision-making by enabling faster, more precise, and automated brain tumor detection, thereby contributing to improved diagnostic reliability and patient outcomes.

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 brain tumor diagnosis, clinicians integrate their medical knowledge and brain magnetic resonance imaging (MRI) scans to obtain the nature and pathological characteristics of brain tumors and to decide on treatment options. However, manually detecting and segmenting brain tumors in today's brain MRI, where a large number of MRI scans taken for each patient, is tedious and subjected to inter and intra observer detection and segmentation variability. As result a number of methods have been proposed in recent years to fill this gap, but still there is no commonly accepted automated technique by clinicians to be used in clinical floor due to accuracy and robustness issues. In our approach, an automatic brain tumor detection and segmentation framework that consists of techniques from skull stripping to detection and segmentation of brain tumors is proposed with fuzzy Hopfield neural network as its final tumor segmentation technique. Through preprocessing, image fusion and initial tumorous slice classification, the final hybrid intelligent fuzzy Hopfield neural network algorithm based tumor segmentation, and tumor region detection and extraction is achieved. The performance of the proposed framework is evaluated on various MR images including simulated and real, normal and tumorous. Quantitatively the method is validated against available ground truth using commonly used validation metrics. The final segmentation mean and standard deviation result in Jaccard similarity index, Dice similarity score, sensitivity and specificity are 0.8569+/-0.0896, 0.9186+/-0.0638, 0.9480+/-0.0402 and 0.9917+/-0.0387 respectively. Quantitative and qualitative segmentation result indicates the potential of the proposed framework.

The brain tumor is a devastating disease, which shortens the life expectancy quite a bit in their highest grade. We aim to present a multiclass brain tumor classification algorithm using deep networks convolution neural networks with loss function as the binary cross-entropy and optimizer as Adam and ResNet50 on multiple datasets of Magnetic Resonance Images. Deep learning has earned a lot of development in the medical field in the last few years. MRI has been a constructive tool for medical research in current years and has many applications like brain tumor detection. In the detection of tumors, MRI images of the brain were classified as tumorous or non-tumorous using deep network CNN. In the classification, contrast-enhanced MR images are used to classify the images into three types of brain tumor: pituitary, glioma, and meningioma. By making use of this, we can identify and give the required medication to the patients.KeywordsBrain tumorDeep learningCNNFigshareTransfer learning

Image segmentation is the fundamental step in medical image analysis. Segmentation is a procedure to separate similar portions of images showing resemblance in different features such as color, intensity, or texture. Grayscale images are mostly used for the segmentation of medical images. Tumors are commonly stated as the abnormal growth of tissues and the brain tumor is a diseased part in the body tissues that is an abnormal mass in which the growth rate of cells is irrepressible. The mortality rate of people has raised over the past years due to brain tumors, hence this area has gained the attention of researchers. Automatic detection of brain tumors is a challenging task because it involves pathology, functional physics of MRI along with intensity and shapes analysis of MR image. After all, tumor shape, size, location, and intensity vary for each infected case. In this work, a novel hybrid approach is implemented by combing watershed segmentation, level set segmentation and K means clustering. First, the image is preprocessed by removing the skull. Watershed segmentation is applied to this preprocessed image. Level set segmentation is applied to the previous step. Finally, k means clustering is applied as the final step to detect tumor parts accurately. This Hybrid approach is compared with other four techniques such as Threshold segmentation, K means clustering, Watershed segmentation, and Level set-based segmentation methods. Statistical and Visual analysis is performed. It is found that the hybrid approach has better specificity, accuracy, and precision among all four techniques. Further, it is able to detect tumors more accurately. This research could help clinicians in surgical planning, treatment planning and accurately segmenting the tumor part with the most accurate method.

Brain tumor disease is a serious and complex health problem worldwide. Early and accurate detection of brain tumors has a major impact on patient care and prognosis. Magnetic Resonance Imaging (MRI) has become one of the main diagnostic tools in detecting brain tumors, manual interpretation of MRI images requires high clinical expertise and requires a long time. In recent years, advances in deep learning techniques and image processing have opened up new opportunities in the detection of brain tumors via MRI images. Deep learning techniques, especially the use of Vision Transformers (ViTs) models, have been successful in various complex pattern recognition tasks in images. The Vision Transformers model was chosen due to the performance improvements shown in many image recognition tasks, outperforming convolutional neural networks (CNN) based methods. Tensorflow and Keras are used as frameworks for development and training models, which have been proven effective and efficient in various previous studies. This study focuses on the performance of the Vision Transformer (ViT) in detecting brain tumors through two Magnetic Resonance Imaging (MRI) image datasets, with different numbers of datasets, as well as the maximum accuracy value that can be achieved from the ViT architecture. From several experimental parameters on ViT, the number of datasets and iterations, the results obtained from the first dataset with 253 image data obtained an accuracy value of 88%, and in the second study by combining the two datasets, with 3.123 data images obtained an accuracy of 97.9%.

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.

The fact that accurate detection of metastatic brain tumors is important for making decisions on the treatment course of patients prompted us to develop a computer-aided diagnostic scheme for detecting metastatic brain tumors. In this paper, we first describe how we extracted the cerebral parenchyma region using a standard deviation filter. Second, initial candidates for tumors were decided by sphericity and cross-correlation value with a simulated ring template. Third, we made true positive and false positive templates obtained from actual clinical images and applied the template matching technique to them. Finally, we detected metastatic tumors using these two characteristics. Our improved method was applied to 13 cases with 97 brain metastases. Sensitivity of detection of metastatic brain tumors was 80.4%, with 5.6 false positives per patient. Our proposed method has potential for detection of metastatic brain tumors in brain magnetic resonance (MR) images.

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.

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.