Classification Of MRI Images Research Articles

Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method

Background/Objectives: The accurate categorization of brain MRI images into tumor and non-tumor categories is essential for a prompt and effective diagnosis. This paper presents a novel methodology utilizing advanced Convolutional Neural Network (CNN) designs to tackle the complexity and unpredictability present in brain MRI data. Methods: The methodology commences with an extensive preparation phase that includes image resizing, grayscale conversion, Gaussian blurring, and the delineation of the brain region for preparing the MRI images for analysis. The Multi-verse Optimizer (MVO) is utilized to optimize data augmentation parameters and refine the configuration of trainable layers in VGG16 and ResNet50. The model’s generalization capabilities are significantly improved by the MVO’s ability to effectively balance computational cost and performance. Results: The amalgamation of VGG16 and ResNet50, further refined by the MVO, exhibits substantial enhancements in classification metrics. The MVO-optimized hybrid model demonstrates enhanced performance, exhibiting a well-calibrated balance between precision and recall, rendering it exceptionally trustworthy for medical diagnostic applications. Conclusions: The results highlight the effectiveness of MVO-optimized CNN models for classifying brain tumors in MRI data. Future investigations may examine the model’s applicability to multiclass issues and its validation in practical clinical environments.

Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification.

Background: Brain tumors are highly complex, making their detection and classification a significant challenge in modern medical diagnostics. The accurate segmentation and classification of brain tumors from MRI images are crucial for effective treatment planning. This study aims to develop an advanced neural network architecture that addresses these challenges. Methods: We propose L-net, a novel architecture combining U-net for tumor boundary segmentation and a convolutional neural network (CNN) for tumor classification. These two units are coupled such a way that the CNN classifies the MRI images based on the features extracted by the U-net while segmenting the tumor, instead of relying on the original input images. The model is trained on a dataset of 3064 high-resolution MRI images, encompassing gliomas, meningiomas, and pituitary tumors, ensuring robust performance across different tumor types. Results: L-net achieved a classification accuracy of up to 99.6%, surpassing existing models in both segmentation and classification tasks. The model demonstrated effectiveness even with lower image resolutions, making it suitable for diverse clinical settings. Conclusions: The proposed L-net model provides an accurate and unified approach to brain tumor segmentation and classification. Its enhanced performance contributes to more reliable and precise diagnosis, supporting early detection and treatment in clinical applications.

Classification and segmentation of kidney MRI images for chronic kidney disease detection

Chronic Kidney Disease (CKD) is a common ailment with significant public health implications, underscoring the critical importance of early detection and diagnosis for effective management. Our research focuses on utilizing deep learning models to accurately classify and segment kidney MRI images, aiding in the timely detection and diagnosis of CKD. Utilizing the Zenodo dataset comprising 1299 kidney MRI images from 100 T2-weighted abdomen MRI scans, our investigation reveals that Vision Transformers (ViT) based model EfficientNet_b1 exhibited superior performance compared to other models. It achieved an accuracy of 94.38% in distinguishing between healthy and CKD cases in kidney MRI image classification. Moreover, the pretrained models Densenet201 and VGG19, as well as the ViT-based models EfficientNet_b1 and tf_mobilenetv3_large_075, demonstrate remarkable sensitivity and accuracy while compared to identifying kidney MRI images which highlights the effectiveness of deep learning architectures in identifying diseases. Additionally, our segmentation analysis reveals that our proposed network, Resnet18-Self-ONN-UNet++, adeptly delineates kidney structures. The network shows remarkable performance in medical diagnosis and treatment planning, with an Intersection over Union (IoU) of 82.34% and DS of 91.57% when integrated with the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique and Simultaneous truth and performance level estimation (STAPLE) as post processing technique. In summary, deep learning-driven kidney MRI image classification and segmentation enhance medical imaging analysis, potentially aiding in early CKD diagnosis and treatment, thereby addressing critical public health concerns, and enhancing patient outcomes.

RESEARCH ON ISCHEMIC STROKE MRI IMAGE CLASSIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK

RESEARCH ON ISCHEMIC STROKE MRI IMAGE CLASSIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK

A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis

Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge. In the RBPNN pattern aggregation layer, the outputs of RBPN are selectively summed according to the category of the kernel center, that is, the subcategory features are combined into category features, and finally the image classification is implemented based on Softmax. The functional module of the proposed method is designed specifically for image characteristics, which can highlight the significance of local and structural features of the image, form a non-convex decision-making area, and reduce the requirements for the completeness of the sample set. Applying the proposed method to medical image classification, experiments were conducted based on the brain tumor MRI image classification public dataset and the actual cardiac ultrasound image dataset, and the accuracy rate reached 85.82% and 83.92% respectively. Compared with the three mainstream image classification models, the performance indicators of this method have been significantly improved.

Explainable Reverse Verification of Goodness of Classification of MRI Images by Clinical Experts.

Radiology offers a presumptive diagnosis. The etiology of radiological errors are prevalent, recurrent, and multi-factorial. The pseudo-diagnostic conclusions can arise from varying factors such as, poor technique, failures of visual perception, lack of knowledge, and misjudgments. This retrospective and interpretive errors can influence and alter the Ground Truth (GT) of Magnetic Resonance (MR) imaging which in turn result in faulty class labeling. Wrong class labels can lead to erroneous training and illogical classification outcomes for Computer Aided Diagnosis (CAD) systems. This work aims at verifying and authenticating the accuracy and exactness of the GT of biomedical datasets which are extensively used in binary classification frameworks. Generally such datasets are labeled by only one radiologist. Our article adheres a hypothetical approach to generate few faulty iterations. An iteration here considers simulation of faulty radiologist's perspective in MR image labeling. To achieve this, we try to simulate radiologists who are subjected to human error while taking decision regarding the class labels. In this context, we swap the class labels randomly and force them to be faulty. The experiments are carried out on some iterations (with varying number of brain images) randomly created from the brain MR datasets. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate our work, average classification parameter values of faulty iterations are compared with that of original dataset. It is presumed that, the presented approach provides a potential solution to verify the genuineness and reliability of the GT of the MR datasets. This approach can be utilized as a standard technique to validate the correctness of any biomedical dataset.

Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images

Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images

This study addresses the challenge of intelligent diagnosis in Alzheimer's Disease by employing machine learning to classify MRI images depicting various disease stages. The author conducted a comparative analysis to assess the efficacy of diverse models

This study addresses the challenge of intelligent diagnosis in Alzheimer's Disease by employing machine learning to classify MRI images depicting various disease stages. The author conducted a comparative analysis to assess the efficacy of diverse models for this purpose, yielding several key findings. The primary model utilized in the research is founded on ResNet152V2, encompassing 152 pre-trained layers, 1 GlobalAveragePooling2D layer, 3 dropout layers, 3 dense layers, and 1 output layer. The research aims to achieve precise classification of MRI images corresponding to distinct Alzheimer's Disease stages, a pivotal step for early detection and intervention. To achieve this, this research adjusted the structure of the original model and changed some of the parameters and compared their results. Based on the results obtained, the author drew several conclusions. The author found that setting a larger value for the "patience" parameter in the Early Stopping callback helps avoid stopping the training process too early. Additionally, the author observed that the original model with only 2 fully connected layers was not stable enough in terms of gradient descent, indicating the need for additional fully connected layers. In conclusion, this research paper addressed the problem of classifying MRI images of different stages of Alzheimer's Disease using machine learning techniques. The comparative study of different models provided insights into the performance and potential improvements of the classification taskt.

Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions

The study is devoted to considering the effectiveness of modern approaches to the development of diagnostic technology for analyzing MRI images in neuro-oncology, based on artificial intelligence (AI) and computer vision. Such approaches are necessary for rapid and diagnostically effective analysis to implement the principle of individualized medicine. Material and methods. An analysis of the effectiveness of the choice of AI technologies for the formation of processes of segmentation and classification of neuro-oncological MRI images has been presented. AI was trained on its own annotated database (SBT Dataset), containing about 1000 clinical cases based on archival data from preoperative MRI studies at the Federal Neurosurgical Center (Novosibirsk, Russian Federation), in patients with astrocytoma, glioblastoma, meningioma, neuroma, and with metastases of somatic tumors, with histological and histochemical postoperative confirmation. Results and discussion. The effectiveness and efficiency of the developed technologies was tested during the international BraTS competition, in which it was proposed to segment and classify cases from a dataset of neuro-oncological patients prepared by the competition organizers. Conclusions. The methodological approaches proposed in the article in the development of diagnostic systems based on AI and the principles of computer vision have shown high efficiency at the level of dozens of world leaders and can be used to develop software and hardware systems for diagnostic neuroradiology with the functions of a “doctor’s assistant.”

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.

Automated classification of MRI images for mild cognitive impairment and Alzheimer’s disease detection using log gabor filters based deep meta-heuristic classification

In older people, mild cognitive impairment (MCI) is a precursor to more severe forms of dementia like AD (AD). In diagnosing patients with primary AD and amnestic MCI, modern neuroimaging techniques, especially MRI, play a key role. To efficiently categorize MRI images as normal or abnormal, the research presents a machine learning-based automatic labelling system, with a focus on boosting performance via texture feature analysis. To this end, the research implements a preprocessing phase employing Log Gabor filters, which are particularly well-suited for spatial frequency analysis. In addition, the research uses Gray Wolf Optimization (GWO) to acquire useful information from the images. For classification tasks using the MRI images, the research also make use of DenseNets, a form of deep neural network. The proposed method leverages Log Gabor filters for preprocessing, Gray Wolf Optimization (GWO) for feature extraction, and DenseNets for classification, resulting in a robust approach for categorizing MRI images as normal or abnormal. When compared to earlier trials performed without optimization, the proposed systematic technique shows a significant increase in classification accuracy of 15%. For neuroimaging applications, our research emphasizes the use of Log Gabor filters for preprocessing, GWO for feature extraction, and DenseNets for classification, which can help with the early detection and diagnosis of MCI and AD.

Advanced Brain Tumor Diagnosis: MRI Image Classification with Deep Learning Technology

Currently several diseases are epidemic in Bangladesh, one of them is brain tumor. This disease usually affects people of any age. It usually spreads slowly in the human brain. Although initially it is small, as time goes by it does not look terrible, and the shape of the head becomes distorted. If the disease continues for a long time and without specific treatment, it often does not turn into cancer. Because the tumor affects the brain, it is called a brain tumor. Using machine learning and various image processing techniques, this paper proposes to automate brain tumor diagnosis. This dataset consists of images of brain tumor patients, non-brain tumors, and a total of 4 types of brain tumors. In this paper we have used the Customize CNN Algorithm, through which we have used the 7 architectures of CNN. The 7 custom CNN methods we used are VGG19, MobileNetV2, MobileNetV3, EfficientNet B0, EfficientNet B3, Inception V3 and DenseNet201. Here we can see that accuracy of VGG19 is 0.75, accuracy of MobileNetV2 is 0.88, accuracy of MobileNetV3 is 0.97, accuracy of EfficientNet B0 is 0.99, accuracy of EfficientNet B3 is 0. 99, Inception V3 is 0.86, and DenseNet201’s accuracy is 0.90. From all the methods it can be seen that EfficientNet B0 and EfficientNet B3 method has obtained the highest accuracy i.e., 0.99. The lowest accuracy i.e., 0.75 was obtained using the VGG19 model.

Enhancing Parkinson's Disease Diagnosis through Deep Learning-Based Classification of 3D MRI Images

This paper presents a deep learning-based model developed to accurately classify Parkinson's disease patients using 3D MRI scans and data augmentation techniques. The study aimed to address the research question of whether this approach could effectively detect structural changes indicative of Parkinson's disease and enhance diagnostic accuracy. The model utilized 3D brain MRI scans from the midbrain region and leveraged the domain of data augmentation to expand the dataset and improve generalizability. The testing accuracy of the model was evaluated with and without data augmentation to further understand it's applicability. The paper demonstrated the effectiveness of the proposed model in classifying Parkinson's disease patients from healthy controls, achieving a testing accuracy of up to 90.13% with data augmentation. The incorporation of data augmentation led to a significant 15.53% increase in accuracy compared to the model without augmentation. The utilization of 3D MRI scans allowed for a more comprehensive assessment of structural changes associated with the disease, and it is intuitive to believe that shuch efficient predictions can facilitate earlier treatment and better patient outcomes.

Deep Learning-based Classification of MRI Images for Early Detection and Staging of Alzheimer's Disease

Deep Learning-based Classification of MRI Images for Early Detection and Staging of Alzheimer's Disease

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

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

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.

Deep learning-based image classification of MRI brain image

This article reviews the latest research on MRI brain image classification techniques based on deep learning. Firstly, MRI brain image classification and traditional machine learning methods applied to MRI image classification were briefly introduced. Then, a review was conducted on existing MRI brain image classification methods based on deep learning. The article reviews past and recent related research and provides a detailed introduction to the application of deep learning methods in MRI brain image classification. This includes some traditional machine learning methods and research achievements in deep neural networks (DNN), convolutional neural networks (CNN), and transfer learning. The most commonly used deep learning architecture for image classification is CNN. Research has shown that deep learning methods have high accuracy and performance in MRI brain image classification and can automatically extract image features for effective classification. Therefore, deep learning methods provide doctors with more comprehensive information, help them make more accurate diagnoses and formulate treatment plans, and have broad prospects for application in MRI brain image classification. In the future, the further development of deep learning technology will achieve better auxiliary effects on diagnosis, treatment, and scientific research related to the brain.

Brain MRI Image Classification and Analysis Using Modified ResNet50V2 for Parkinson’s Disease Detection

Brain MRI Image Classification and Analysis Using Modified ResNet50V2 for Parkinson’s Disease Detection

Retracted: DenseNet-Based Classification of MRI Images for Detecting the Difference before and after Treating Liver Cancer

Retracted: DenseNet-Based Classification of MRI Images for Detecting the Difference before and after Treating Liver Cancer