MRI Brain Tumor Images Research Articles

Diagnosing Brain Tumors from MRI images through a Multi-Fused CNN with Auxiliary Layers

In this study, we proposed a novel Multi-Fused Residual Convolutional Neural Network (MFR-CNN) with Auxiliary Fusing Layers (AuxFL) to diagnose various types of brain tumor MRI images. The MFR-CNN was designed to handle four specific cases, namely glioma, meningioma, pituitary, and healthy brain images, obtained from reliable Kaggle databases. Our proposed model integrated three state-of-the-art models into a single feature extraction pipeline, incorporating partially frozen and truncated layers. This strategic fusion enabled the propagation of robust features and improved diagnostic performance without incurring significant computing costs, unlike most existing state-of-the-art models. Moreover, the MFR-CNN effectively mitigated overfitting and performance saturation issues, providing a notable advantage over models lacking these components. Upon evaluation, our proposed model achieved an outstanding accuracy of 94%, surpassing the efficiency and accuracy of conventionally trained DCNNs. Notably, the MFR-CNN demonstrated potential in enhancing brain tumor diagnosis more cost-efficiently than ensembles and outperforming conventional pre-trained and fine-tuned DCNNs. In conclusion, the proposed MFR-CNN with AuxFL and FuRB exhibits promising capabilities to improve the diagnosis of brain tumors, offering better cost-efficiency and accuracy compared to existing methods.

CorrDiff: Corrective Diffusion Model for Accurate MRI Brain Tumor Segmentation.

Accurate segmentation of brain tumors in MRI images is imperative for precise clinical diagnosis and treatment. However, existing medical image segmentation methods exhibit errors, which can be categorized into two types: random errors and systematic errors. Random errors, arising from various unpredictable effects, pose challenges in terms of detection and correction. Conversely, systematic errors, attributable to systematic effects, can be effectively addressed through machine learning techniques. In this paper, we propose a corrective diffusion model for accurate MRI brain tumor segmentation by correcting systematic errors. This marks the first application of the diffusion model for correcting systematic segmentation errors. Additionally, we introduce the Vector Quantized Variational Autoencoder (VQ-VAE) to compress the original data into a discrete coding codebook. This not only reduces the dimensionality of the training data but also enhances the stability of the correction diffusion model. Furthermore, we propose the Multi-Fusion Attention Mechanism, which can effectively enhances the segmentation performance of brain tumor images, and enhance the flexibility and reliability of the corrective diffusion model. Our model is evaluated on the BRATS2019, BRATS2020, and Jun Cheng datasets. Experimental results demonstrate the effectiveness of our model over state-of-the-art methods in brain tumor segmentation.

Classification of brain tumor MRI images using convolutional neural network

Classification of brain tumor MRI images using convolutional neural network

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

Manual prediction of brain tumors is a time-consuming and subjective task, reliant on radiologists' expertise, leading to potential inaccuracies. In response, this study proposes an automated solution utilizing a Convolutional Neural Network (CNN) for brain tumor classification, achieving an impressive accuracy of 98.89%. Following classification, a hybrid approach, integrating graph-based and threshold segmentation techniques, accurately locates the tumor region in magnetic resonance (MR) brain images across sagittal, coronal, and axial views. Comparative analysis with existing research papers validates the effectiveness of the proposed method, and similarity coefficients, including a Bfscore of 1 and a Jaccard similarity of 93.86%, attest to the high concordance between segmented images and ground truth.

An intelligent LinkNet-34 model with EfficientNetB7 encoder for semantic segmentation of brain tumor

A brain tumor is an unnatural expansion of brain cells that can’t be stopped, making it one of the deadliest diseases of the nervous system. The brain tumor segmentation for its earlier diagnosis is a difficult task in the field of medical image analysis. Earlier, segmenting brain tumors was done manually by radiologists but that requires a lot of time and effort. Inspite of this, in the manual segmentation there was possibility of making mistakes due to human intervention. It has been proved that deep learning models can outperform human experts for the diagnosis of brain tumor in MRI images. These algorithms employ a huge number of MRI scans to learn the difficult patterns of brain tumors to segment them automatically and accurately. Here, an encoder-decoder based architecture with deep convolutional neural network is proposed for semantic segmentation of brain tumor in MRI images. The proposed method focuses on the image downsampling in the encoder part. For this, an intelligent LinkNet-34 model with EfficientNetB7 encoder based semantic segmentation model is proposed. The performance of LinkNet-34 model is compared with other three models namely FPN, U-Net, and PSPNet. Further, the performance of EfficientNetB7 used as encoder in LinkNet-34 model has been compared with three encoders namely ResNet34, MobileNet_V2, and ResNet50. After that, the proposed model is optimized using three different optimizers such as RMSProp, Adamax and Adam. The LinkNet-34 model has outperformed with EfficientNetB7 encoder using Adamax optimizer with the value of jaccard index as 0.89 and dice coefficient as 0.915.

YOLO‐UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image

Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the tumor. The clear shape and boundary of the tumor will increase the probability of safe medical surgery. Analysis of this different scope of image types requires refined computerized quantification and visualization tools. This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. The fundamental finding is to detect and localize the tumor area with YOLO‐CNN and segment it with the FCN‐UNet architecture. This analysis provided automatic detection and segmentation as well as the location of the tumor. The segmentation using the UNet is run under four scenarios, and the best one is chosen by the minimum loss and maximum accuracy value. In this research, we used 277 images for training, 69 images for validation, and 14 images for testing. The validation is carried out by comparing the segmentation results with the medical ground truth to provide the correct classification ratio (CCR). This study succeeded in the detection of brain tumors and provided a clear area of the brain tumor with a high CCR of about 97%.

Improving Brain Tumor MRI Image Classification Prediction based on Fine-tuned MobileNet

Improving Brain Tumor MRI Image Classification Prediction based on Fine-tuned MobileNet

DAUnet: A U-shaped network combining deep supervision and attention for brain tumor segmentation

In MRI images, the brain tumor area varies greatly between individuals, and only relying on the judgment of clinicians is prone to misdiagnosis and misjudgment. Consequently, utilizing computer-aided diagnosis is of utmost significance in assisting clinicians with delineating the tumor region. Brain tumor MRI images are 3D images, and traditional segmentation methods tend to lose key information. Therefore, this paper proposes DAUnet, an U-shaped network for brain tumor MRI image segmentation combining deep supervision and convolutional attention. First, a module consisting of a Bottleneck module and attention (BA) module is designed. Here the attention not only uses spatial and channel (SC) attention but also adds residual connection, which is called 3D SC attention. Second, to enlarge the feature map receptive field without changing its resolution, a module consists of standard convolution and atrous spatial pyramid (CASP) module is designed. The feature map information is adjusted by standard convolution, subsequently, the feature map is provided as input to the ASP module. The CASP module fuses the features extracted by downsampling and performs upsampling operation, which strengthens the correlation between different layers of the network. Finally, using deep supervision as an auxiliary branch of the U-shaped network, it combines deep learning and regularization techniques to supervise the model during training, automatically finer parameters, and make the model fit better. Through experiments on BraTS 2020 and FeTS 2021 and comparison with other advanced methods, it has been demonstrated that DAUnet achieves precise segmentation of tumor regions in brain MRI images.

Brain tumor MRI images classification based on machine learning

Recent research has shown machine learnings outstanding performance on image classifying tasks, including applications on Magnetic Resonance Images. While the former models are overly complicated, this paper proposes a simplified model, which is proven to be both accurate and much less time-consuming. Our proposed method is learned from former research and combines Bias Field Correction, DenseNet, and SE-Net to form a concise structure. With small datasets of T1-weighted and T2-weighted labeled MR brain tumor images, our model spent a short training time of 2 hours and showed excellent performance on classifying pituitary, meningioma, glioma or no tumor with an accuracy of 91.32%. After evaluation, our model is proven to be accurate in distinguishing between 3 of the tumor types with an f1-score of 0.96.

Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial networks

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

The symmetrical segmentation of brain tumor images is crucial for both clinical diagnosis and computer-aided prognosis. Traditional manual methods are not only asymmetrical in terms of efficiency but also prone to errors and lengthy processing. A significant barrier to the process is the complex interplay between the deep learning network for MRI brain tumor imaging and the harmonious compound of both local and global feature information, which can throw off the balance in segmentation accuracy. Addressing this asymmetry becomes essential for precise diagnosis. In answer to this challenge, we introduce a balanced, end-to-end solution for brain tumor segmentation, incorporating modifications that mirror the U-Net architecture, ensuring a harmonious flow of information. Beginning with symmetric enhancement of the visual quality of MRI brain images, we then apply a symmetrical residual structure. By replacing the convolutional modules in both the encoder and decoder sections with deep residual modules, we establish a balance that counters the vanishing gradient problem commonly faced when the network depth increases. Following this, a symmetrical threefold attention block is integrated. This addition ensures a balanced fusion of local and global image features, fine-tuning the network to symmetrically discern and learn essential image characteristics. This harmonious integration remarkably amplifies the network’s precision in segmenting MRI brain tumors. We further validate the equilibrium achieved by our proposed model using three brain tumor segmentation datasets and four metrics and by juxtaposing our model against 21 traditional and learning-based counterparts. The results confirm that our balanced approach significantly elevates performance in the segmentation of MRI brain tumor images without an asymmetrical increase in computational time.

Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data

In modern healthcare, the precision of medical image segmentation holds immense significance for diagnosis and treatment planning. Deep learning techniques, such as CNNs, UNETs, and Transformers, have revolutionized this field by automating the previously labor-intensive manual segmentation processes. However, challenges like intricate structures and indistinct features persist, leading to accuracy issues. Researchers are diligently addressing these challenges to further unlock the potential of medical image segmentation in healthcare transformation. To enhance the precision of brain tumor MRI image segmentation, our study introduces three novel feature-enhanced hybrid UNet models (FE-HU-NET): FE1-HU-NET, FE2-HU-NET, and FE3-HU-NET. Our approach encompasses three main aspects. Initially, we emphasize feature enhancement during the image preprocessing stage. We apply distinct image enhancement techniques—CLAHE, MHE, and MBOBHE—to each model. Secondly, we tailor the architecture of the UNet model to enhance segmentation results, focusing on a personalized layered design. Lastly, we employ a CNN model in post-processing to refine segmentation outcomes through additional convolutional layers. The HU-Net module, shared across the three models, integrates a customized UNet layer and a CNN. We also introduce an alternative feature-enhanced variant, FE4-HU-NET, utilizing the DeepLABv3 model. Incorporating CLAHE for image enhancement and bolstered by CNN layers, this variant offers a distinct approach. Rigorous experimentation underscores the excellence of our proposed framework in distinguishing complex brain tissues, surpassing current state-of-the-art models. Impressively, we achieve accuracy rates exceeding 99% across two publicly available datasets. Performance metrics such as the Jaccard index, sensitivity, and specificity further substantiate the effectiveness of our Hybrid U-Net model.

Smart Brain Tumor Prediction System

Brain is the regulatory unit in human body. It controls the functions such as memory, vision, hearing, knowledge, personality, problem solving, etc. The main reason for brain tumor is the abandoned progress of brain cells. Many health organizations have recognized brain tumor as the second foremost dispute that causes many human deaths all around the world. Identification of brain tumor at a premature stage offers opportunity of effective medical treatment. Use of Magnetic Resonance Imaging images have been recognized as more detailed and more consistent images when compared to Computed Tomography images. In this project we are predicting the brain tumor using machine learning algorithms. there are various techniques to detect brain tumor or neoplasms. The most competent and effective algorithm that we will be using in this project is CNN (Convolutional Neural Network).It is a Deep Learning Algorithm that is Used for Image Processing Starting with Pre-processing brain images then, segmenting them, feature extraction of unwanted noisy and blurry part, clustering and detection of the tumor are the methodologies of our Project. Key Word: Brain Tumor Prediction, MRI Images, CNN, Image Processing and Deep Learning.

Brain Tumor Classification Using Deep Neural Network and Transfer Learning

In the field of medical imaging, the classification of brain tumors based on histopathological analysis is a laborious and traditional approach. To address this issue, the use of deep learning techniques, specifically Convolutional Neural Networks (CNNs), has become a popular trend in research and development. Our proposed solution is a novel Convolutional Neural Network that leverages transfer learning to classify brain tumors in MRI images as benign or malignant with high accuracy. We evaluated the performance of our proposed model against several existing pre-trained networks, including Res-Net, Alex-Net, U-Net, and VGG-16. Our results showed a significant improvement in prediction accuracy, precision, recall, and F1-score, respectively, compared to the existing methods. Our proposed method achieved a benign and malignant classification accuracy of 99.30 and 98.40% using improved Res-Net 50. Our proposed system enhances image fusion quality and has the potential to aid in more accurate diagnoses.

BRAIN MRI SEGMENTATION MODELLING USING DEEP LEARNING

The human body is made up of many organs and brain is the most critical and vital organ of them all. One of the common reasons for dysfunction of brain is brain tumor. A tumor is nothing but excess cells growing in an uncontrolled manner Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. The brain tumor detection and classification system is available so that it can be diagnosed at early stages. Cancer classification is the most challenging tasks in clinical diagnosis. This project deals with such a system, which uses computer, based procedures to detect tumor blocks and classify the type of tumor using Convolution Neural Network Algorithm for MRI images of different patients. Different types of image processing techniques like image segmentation, image enhancement and feature extraction are used for the brain tumor detection in the MRI images of the cancer-affected patients. Detecting Brain tumor using Image Processing techniques its involves the four stages is Image Pre-Processing, Image segmentation, Feature Extraction, and Classification. Image processing and neural network techniques are used for improve the performance of detecting and classifying brain tumor in MRI images.

Brain tumor segmentation by cascaded multiscale multitask learning framework based on feature aggregation

Brain tumor analysis in MRI images is a significant and challenging issue because misdiagnosis can lead to death. Diagnosis and evaluation of brain tumors in the early stages increase the probability of successful treatment. However, the complexity and variety of tumors, shapes, and locations make their segmentation and classification complex. Numerous researchers have proposed brain tumor segmentation and classification methods in this regard. This paper presents an approach that simultaneously segments and classifies brain tumors in MRI images using a framework that contains MRI image enhancement and tumor region detection. Eventually, a network based on a multitask learning approach is proposed. The proposed network, called Multiscale Cascaded Multitask Network, is based on a multitask learning approach containing segmentation and classification tasks. A multiscale approach and cascade approach in layers of encoder and decoder have been applied to improve segmentation accuracy in the proposed network. In addition, to increase the classification accuracy, a feature aggregation module has been introduced that integrates different levels of features to better tumor type classification. Simultaneously learning the two tasks of segmentation and classification, along with applying the mentioned approaches, has improved the results in both tasks. Subjective and objective results indicate that the segmentation and classification results based on evaluation metrics are better or comparable to the state-of-the-art. Our proposed method has reached 96.27 and 95.88 for DCS and mean IoU, respectively, for segmentation and 97.988 accuracies for classification.

AlexNet‐NDTL: Classification of MRI brain tumor images using modified AlexNet with deep transfer learning and Lipschitz‐based data augmentation

AbstractDeep learning is frequently used to classify medical images. Surgeons may know the type of tumor before doing surgery on a patient. Transfer learning was used to alleviate the overfitting issue of deep networks in classification since the training samples, such as a brain MRI dataset, were insufficient. To overcome this issue, We introduce a new deep‐learning methodology for the categorization of MRI brain tumor images. This method combines a unique data augmentation model with modified AlexNet and network‐based deep transfer learning. We used Lipschitz‐based data augmentation on a dataset, and the output of the augmentation model was fed into a modified AlexNet that uses network‐based deep transfer learning to extract features from a dataset. The proposed model is trained and tested using the BraTS 2020 and Figshare datasets. The proposed model's performance is assessed using sensitivity, specificity, accuracy, precision, F1‐score, and MCC. The proposed model yields superior results.

An improved GABOR wavelet transform and rough k-means clustering algorithm for MRI BRAIN tumor image segmentation

An improved GABOR wavelet transform and rough k-means clustering algorithm for MRI BRAIN tumor image segmentation

RETRACTED ARTICLE: Analysis of MRI brain tumor images using deep learning techniques

RETRACTED ARTICLE: Analysis of MRI brain tumor images using deep learning techniques

Performance Analysis of Classification and Detection of Brain Tumor MRI Images Using Resnet50 Deep Residual U-Net

Background: This research adds to the growing body of work demonstrating the vital role of image categorization in the medical sector. The efficiency of illness diagnosis is being greatly enhanced using image classification. A brain tumor is a collection of abnormal cells in the brain. Diagnostic precision is required when looking for a tumor in the brain because even the smallest error in human judgement can have disastrous results. To get around this problem, the medical community has implemented an automated brain tumor segmentation system. A variety of methods are employed to segment a brain tumor, but the results are inconsistent. To improve the quality of MRI images, we present our findings in this paper. Deep learning methods for image segmentation and classification are discussed in this paper.
 Methods: In this paper we a very robust deep learning method for image segmentation and classification. For image classification, we are employing the enhanced faster R-CNN method. The ResNet50 model is used to differentiate between tumor and non-tumor images. The next step in this framework is to use Deep Residual UNET for segmentation. RESUNET is an FCNN that maximizes efficiency. The proposed method works well in terms of its ability to find and classify things accurately.
 Results: The accuracy rate for identifying tumours in MRI scans using the proposed technique is 94.23%. Using transfer learning with Resnet50 as the base model and the use of discriminative learning rates, our model achieved superior results than other recent models.
 Conclusion: Within the scope of this study, we have integrated the residual networks with the U-Net to make the network stronger. This strategy improves the efficiency of classification and segmentation tools. To achieve a higher level of accuracy, the model may be trained further, or additional data may be applied in the training process.