MRI-Guided Brain Tumor Detection UsingYOLOv8 and Adaptive Hybrid Transfer Learning

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

Brain tumor is a type of cancer which causes abnormal cells growth in the brain. It can be cured if we detect the brain tumor at an early stage. Brain tumor patients often suffer from blood clot, movement control loss, vision loss, behavioral changes, hormone changes, etc. The location, type, and size of the tumor have an effect on the normal functioning of the individual. To examine the size, shape, and location of tumor in the brain, Magnetic Resonance Imaging (MRI) is used. MRI image produces a clear anatomical view of brain and any small abnormality of brain is perceptible by MRI image. Brain tumor classification, detection, and segmentation are huge concerns of researchers. Artificial Intelligence (AI)-based methods can be used to detect brain tumor. Among various deep learning-based algorithms CNN acquired a better position in image classification. CNN classification accuracy depends on some network parameters as convolutional filters, rectification functions, polling functions, and iteration numbers, etc. It is a problem to determine effective values of the convolution filter size, number, and convolution stride for better accuracy in classification. CNN has the capabilities to produce and learn effective features on large datasets. In this paper, we are using convolutional neural network model which takes the feature maps preprocessed to classify the MRI brain image datasets. This paper uses deep learning which processes NIFTI images and creates 3D convolutional neural network, which can be trained to detect and segment structures if corresponding ground truth labels are provided for training.

Feature mining for encrypted malicious traffic detection with deep learning and other machine learning algorithms

The purpose of this retrospective study was to determine the magnetic resonance imaging (MRI) characteristics of feline brain tumors and to determine whether these characteristics can be used to accurately predict the histologic diagnosis. MRI scans of 46 cats with histologically confirmed brain tumors were reviewed, including 33 meningiomas, 6 lymphomas, 4 gliomas, 2 olfactory neuroblastomas, and 1 pituitary tumor. MRI features including axial origin, shape, location, signal intensity, contrast enhancement, peritumoral edema, and mass effect were reviewed and characterized for each tumor type. Tumor shape, axial origin, contrast enhancement, and degree of peritumoral edema aided in the identification of tumor type. Meningiomas were always extra-axial and were most often ovoid with marked contrast enhancement and mild peritumoral edema. Gliomas were always intra-axial with ring enhancement and generally caused more peritumoral edema than other tumors. The brain tumor was detected on MRI in 45 (98%) cats. Two blinded independent reviewers correctly identified 82% of all of the tumor types on the basis of MRI appearance alone. Thus, MRI is an excellent diagnostic tool for the detection of brain tumors in cats, and it provides important information to aid in the diagnosis of tumor type.

Abstract: A brain tumour is described by the presence of abnormal cells in the brain's tissues. Brain tumours can be benign (not cancerous) or malignant (cancerous). The malignant brain tumour is one of the leading and common cancers in the world. There are two types of tumours, primary tumours that develop in the brain and secondary tumours that start in another region of the body and then spread to the brain. The precise identification of the size and location of a brain tumour is crucial in the diagnosis of a brain tumour and is often diagnosed with magnetic resonance imaging (MRI). This book chapter discusses the major types of brain tumours and the advancements in computeraided approaches for detecting brain tumours. The manuscript gives an overview of various recent machine learning and medical image processing approaches developed recently for the identification and classification of brain tumours. Several medical image dataset available for the research works in this domain is also briefed in this article. The major research challenges which can be addressed by the researchers in the domain of brain tumour detection are also discussed in this article. Method: This manuscript mainly discusses various medical image processing methods to detect brain tumors. Results: The manuscript discusses the efficiency of the existing schemes and the key areas where further improvement is required Conclusion: This manuscript gives an overview of various categories of brain tumors, existing methods to detect brain tumors, the critical challenges in this domain and the medical image dataset available for the study.

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

A brain tumor is the cause of abnormal growth of cells in the brain. Magnetic resonance imaging (MRI) is the most practical method for detecting brain tumors. Through these MRIs, doctors analyze and identify abnormal tissue growth and can confirm whether the brain is affected by a tumor or not. Today, with the emergence of artificial intelligence techniques, the detection of brain tumors is done by applying the techniques and algorithms of machine learning and deep learning. The advantages of the application of these algorithms are the quick prediction of brain tumors, fewer errors, and greater precision, which help in decision-making and in choosing the most appropriate treatment for patients. In the proposed work, a convolution neural network (CNN) is applied with the aim of detecting the presence of a brain tumor and its performance is analyzed. The main purpose of this article is to adopt the approach of convolutional neural networks as a machine learning technique to perform brain tumor detection and classification. Based on training and testing results, the pre-trained architecture model reaches 96% in precision and classification accuracy rates. For the given dataset, CNN proves to be the better technique for predicting the presence 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.

Brain tumor segmentation in magnetic resonance imaging (MRI) has become emanant research area in the field of medical imaging system. To find the exact size and location of tumor, Brain tumor detection is very useful. K-means clustering algorithm is proposed in this paper for tumor detection based on segmentation and morphological operation. Firstly, MRI scanned image is pre-processed. The image is then subjected to the K-means clustering after that morphological operator is applied to extract the tumor in the pre-processed MRI scanned image. Finally, the area of extracted tumor part is calculated.

Brain tumors are abnormal tissue that grow uncontrolled and affect a patient's neurological function. Brain tumors come in different shapes and characteristics. Moreover, its location also differs for each patient. Brain tumors can be detected using machine learning algorithms using magnetic resonance imaging (MRI) images. However, a different machine-learning comparison is limited and needs further investigation. This study aims to compare three machine-learning methods, i.e., Support Vector Machine (SVM), Naive Bayes (NB), and Artificial Neural Network Back Propagation (ANN-BP) algorithms for detecting brain tumors. Before the comparison started, MRI image quality was enhanced by performing denoising, histogram equalization, and thresholding. After that, Gray Level Co-occurrence Matrix feature extraction was performed. MRI brain images in JPEG format were acquired from an open-access database. One thousand brain tumor and 1000 normal tumor images are used as the training data, while 100 brain tumor and 100 normal tumor images are used as testing data. Each algorithm's accuracy, precision, sensitivity, and Matthews Correlation Coefficient (MCC) are evaluated and reported. The study showed that the SVM algorithm acquired the highest performance in detecting brain tumors, followed by ANN-BP and NB. The highest accuracy, precision, sensitivity, and MCC values for testing in SVM were 98,75%, 98,22%, 99,30%, and 0,9751, respectively. Meanwhile, in testing, the highest accuracy, precision, sensitivity, and MCC values were 90.50%, 98.80%, 82.00%, and 0.8220, respectively. In conclusion, this study showed the superiority of the SVM algorithm in detecting brain tumor compared to ANN-BP and NB by performing image enhancement steps and GLCM feature extraction before its detection.

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

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, magnetic resonance imaging (MRI) scans are frequently employed to identify tumors. The manual identification of tumors is difficult and requires considerable time due to the large number of three-dimensional images that an MRI scan of one patient's brain produces from various angles. Moreover, the variations in location, size, and shape of the brain tumor also make it challenging to detect and classify different types of tumors. Thus, computer-aided diagnostics (CAD) systems have been proposed for the detection of brain tumors. In this paper, we proposed a novel unified end-to-end deep learning model named TumorDetNet for brain tumor detection and classification. Our TumorDetNet framework employs 48 convolution layers with leaky ReLU (LReLU) and ReLU activation functions to compute the most distinctive deep feature maps. Moreover, average pooling and a dropout layer are also used to learn distinctive patterns and reduce overfitting. Finally, one fully connected and a softmax layer are employed to detect and classify the brain tumor into multiple types. We assessed the performance of our method on six standard Kaggle brain tumor MRI datasets for brain tumor detection and classification into (malignant and benign), and (glioma, pituitary, and meningioma). Our model successfully identified brain tumors with remarkable accuracy of 99.83%, classified benign and malignant brain tumors with an ideal accuracy of 100%, and meningiomas, pituitary, and gliomas tumors with an accuracy of 99.27%. These outcomes demonstrate the potency of the suggested methodology for the reliable identification and categorization of brain tumors.

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Brain tumor detection using machine learning techniques seems to be a promising approach to improve the accuracy and efficiency of the diagnosis process. This research proposes a hybrid brain tumor detection scheme using convolutional neural networks (CNN) and BAT algorithms. This method involves the use of medical imaging, such as magnetic resonance imaging (MRI) or computed tomography (CT) scans, to detect and classify brain tumors. The removal of the past and its negative repercussions from diverse pixy units causes age extraction. By mapping the contour and c-label of the tumor and its limitations, we can acquire information about the tumor that could aid in diagnosis. This approach may be used to identify the tumor's size, shape, and function. The clinical team and the patient can detect the significance of the tumor by using different color labeling different levels of elevation. The clinical workforce can send data to a graphical user interface for the shape and limitations of the tumor. The CNN is trained to identify patterns and features in the images that are indicative of a tumor, while the bat algorithm is used to optimize the parameters of the CNN for improved performance. This approach has the potential to increase the accuracy and speed of brain tumor diagnosis.

Deep learning models for brain tumour diseases identification have grown in importance for accurate and efficient medical imaging diagnosis. In this study, the EfficientNetB3 architecture used, a cutting-edge convolutional neural network (CNN) noted for its efficiency and performance, to create a multiclass brain tumour detection system. The goal is to categorise brain MRI scans into four types: no tumour, glioma, meningioma, and pituitary tumour. The effective use of EfficientNetB3 for brain tumour diagnosis demonstrates the utility of advanced deep learning architectures in medical picture processing. EfficientNetB3 can help healthcare practitioners diagnose and treat brain tumours, allowing for more timely treatments and personalised patient care. this study illustrates the revolutionary influence of deep learning in neuro-oncology and paves the door for improved diagnostic capabilities in brain tumour detection.