A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm

Introduction

Brain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process, Magnetic Resonance Imaging (MRI) has been extensively used in disease detection. However, the accurate identification of BT is a complex task, and conventional techniques are not sufficiently robust to localize and extract tumors in MRI images. Therefore, in this study, we used a deep learning model combined with a segmentation algorithm to localize and extract tumors from MR images.

Method

This paper presents a Deep Learning (DL)-based You Look Only Once (YOLOv7) model in combination with the Grab Cut algorithm to extract the foreground of the tumor image to enhance the detection process. YOLOv7 is used to localize the tumor region, and the Grab Cut algorithm is used to extract the tumor from the localized region.

Results

The performance of the YOLOv7 model with and without the Grab Cut algorithm is evaluated. The results show that the proposed approach outperforms other techniques, such as hybrid CNN-SVM, YOLOv5, and YOLOv6, in terms of accuracy, precision, recall, specificity, and F1 score.

Discussion

Our results show that the proposed technique achieves a high dice score between tumor-extracted images and ground truth images. The findings show that the performance of the YOLOv7 model is improved by the inclusion of the Grab Cut algorithm compared to the performance of the model without the algorithm.

Advances in Mass Spectrometry of Gangliosides Expressed in Brain Cancers

Gangliosides are highly abundant in the human brain where they are involved in major biological events. In brain cancers, alterations of ganglioside pattern occur, some of which being correlated with neoplastic transformation, while others with tumor proliferation. Of all techniques, mass spectrometry (MS) has proven to be one of the most effective in gangliosidomics, due to its ability to characterize heterogeneous mixtures and discover species with biomarker value. This review highlights the most significant achievements of MS in the analysis of gangliosides in human brain cancers. The first part presents the latest state of MS development in the discovery of ganglioside markers in primary brain tumors, with a particular emphasis on the ion mobility separation (IMS) MS and its contribution to the elucidation of the gangliosidome associated with aggressive tumors. The second part is focused on MS of gangliosides in brain metastases, highlighting the ability of matrix-assisted laser desorption/ionization (MALDI)-MS, microfluidics-MS and tandem MS to decipher and structurally characterize species involved in the metastatic process. In the end, several conclusions and perspectives are presented, among which the need for development of reliable software and a user-friendly structural database as a search platform in brain tumor diagnostics.

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification

Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time-frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time-frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists.

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI

The unnatural and uncontrolled increase of brain cells is called brain tumors, leading to human health danger. Magnetic resonance imaging (MRI) is widely applied for classifying and detecting brain tumors, due to its better resolution. In general, medical specialists require more details regarding the size, type, and changes in small lesions for effective classification. The timely and exact diagnosis plays a major role in the efficient treatment of patients. Therefore, in this research, an efficient hybrid optimization algorithm is implemented for brain tumor segmentation and classification. The convolutional neural network (CNN) features are extracted to perform a better classification. The classification is performed by considering the extracted features as the input of the deep residual network (DRN), in which the training is performed using the proposed chronological Jaya honey badger algorithm (CJHBA). The proposed CJHBA is the integration of the Jaya algorithm, honey badger algorithm (HBA), and chronological concept. The performance is evaluated using the BRATS 2018 and Figshare datasets, in which the maximum accuracy, sensitivity, and specificity are attained using the BRATS dataset with values 0.9210, 0.9313, and 0.9284, respectively.

BMRI-NET: A Deep Stacked Ensemble Model for Multi-class Brain Tumor Classification from MRI Images

Brain tumors are one of the most dangerous health problems for adults and children in many countries. Any failure in the diagnosis of brain tumors may lead to shortening of human life. Accurate and timely diagnosis of brain tumors provides appropriate treatment to increase the patient's chances of survival. Due to the different characteristics of tumors, one of the challenging problems is the classification of three types of brain tumors. With the advent of deep learning (DL) models, three classes of brain tumor classification have been addressed. However, the accuracy of these methods requires significant improvements in brain image classification. The main goal of this article is to design a new method for classifying the three types of brain tumors with extremely high accuracy. In this paper, we propose a novel deep stacked ensemble model called "BMRI-NET" that can detect brain tumors from MR images with high accuracy and recall. The stacked ensemble proposed in this article adapts three pre-trained models, namely DenseNe201, ResNet152V2, and InceptionResNetV2, to improve the generalization capability. We combine decisions from the three models using the stacking technique to obtain final results that are much more accurate than individual models for detecting brain tumors. The efficacy of the proposed model is evaluated on the Figshare brain MRI dataset of three types of brain tumors consisting of 3064 images. The experimental results clearly highlight the robustness of the proposed BMRI-NET model by achieving an overall classification of 98.69% and an average recall, F1-score and MCC of 98.33%, 98.40, and 97.95%, respectively. The results indicate that the proposed BMRI-NET model is superior to existing methods and can assist healthcare professionals in the diagnosis of brain tumors.

Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm

One of the worst diseases is a brain tumor, which is defined by abnormal development of synapses in the brain. Early detection of brain tumors is essential for improving prognosis, and classifying tumors is a vital step in the disease's treatment. Different classification strategies using deep learning have been presented for the diagnosis of brain tumors. However, several challenges exist, such as the need for a competent specialist in classifying brain cancers by deep learning models and the problem of building the most precise deep learning model for categorizing brain tumors. We propose an evolved and highly efficient model based on deep learning and improved metaheuristic algorithms to address these challenges. Specifically, we develop an optimized residual learning architecture for classifying multiple brain tumors and propose an improved variant of the Hunger Games Search algorithm (I-HGS) based on combining two enhancing strategies: Local Escaping Operator (LEO) and Brownian motion. These two strategies balance solution diversity and convergence speed, boosting the optimization performance and staying away from the local optima. First, we have evaluated the I-HGS algorithm on the IEEE Congress on Evolutionary Computation held in 2020 (CEC'2020) test functions, demonstrating that I-HGS outperformed the basic HGS and other popular algorithms regarding statistical convergence, and various measures. The suggested model is then applied to the optimization of the hyperparameters of the Residual Network 50 (ResNet50) model (I-HGS-ResNet50) for brain cancer identification, proving its overall efficacy. We utilize several publicly available, gold-standard datasets of brain MRI images. The proposed I-HGS-ResNet50 model is compared with other existing studies as well as with other deep learning architectures, including Visual Geometry Group 16-layer (VGG16), MobileNet, and Densely Connected Convolutional Network 201 (DenseNet201). The experiments demonstrated that the proposed I-HGS-ResNet50 model surpasses the previous studies and other well-known deep learning models. I-HGS-ResNet50 acquired an accuracy of 99.89%, 99.72%, and 99.88% for the three datasets. These results efficiently prove the potential of the proposed I-HGS-ResNet50 model for accurate brain tumor classification.

Sensor Data Prediction in Missile Flight Tests

Sensor data from missile flights are highly valuable, as a test requires considerable resources, but some sensors may be detached or fail to collect data. Remotely acquired missile sensor data are incomplete, and the correlations between the missile data are complex, which results in the prediction of sensor data being difficult. This article proposes a deep learning-based prediction network combined with the wavelet analysis method. The proposed network includes an imputer network and a prediction network. In the imputer network, the data are decomposed using wavelet transform, and the generative adversarial networks assist the decomposed data in reproducing the detailed information. The prediction network consists of long short-term memory with an attention and dilation network for accurate prediction. In the test, the actual sensor data from missile flights were used. For the performance evaluation, the test was conducted from the data with no missing values to the data with five different missing rates. The test results showed that the proposed system predicts the missile sensor most accurately in all cases. In the frequency analysis, the proposed system has similar frequency responses to the actual sensors and showed that the proposed system accurately predicted the sensors in both tendency and frequency aspects.

An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. Decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources.

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k-neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.

BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification

Early detection of brain tumors can save precious human life. This work presents a fully automated design to classify brain tumors. The proposed scheme employs optimal deep learning features for the classification of FLAIR, T1, T2, and T1CE tumors. Initially, we normalized the dataset to pass them to the ResNet101 pretrained model to perform transfer learning for our dataset. This approach results in fine-tuning the ResNet101 model for brain tumor classification. The problem with this approach is the generation of redundant features. These redundant features degrade accuracy and cause computational overhead. To tackle this problem, we find optimal features by utilizing differential evaluation and particle swarm optimization algorithms. The obtained optimal feature vectors are then serially fused to get a single-fused feature vector. PCA is applied to this fused vector to get the final optimized feature vector. This optimized feature vector is fed as input to various classifiers to classify tumors. Performance is analyzed at various stages. Performance results show that the proposed technique achieved a speedup of 25.5x in prediction time on the medium neural network with an accuracy of 94.4%. These results show significant improvement over the state-of-the-art techniques in terms of computational overhead by maintaining approximately the same accuracy.

Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification

Purpose

Glioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improving the performance of glioma subtype classification using multimodal MRI images.

Method

MRI images of four modalities for 221 glioma patients were collected from Computational Precision Medicine: Radiology-Pathology 2020 challenge, including T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) MRI images, to classify astrocytoma, oligodendroglioma, and glioblastoma. We proposed a multimodal MRI image decision fusion-based network for improving the glioma classification accuracy. First, the MRI images of each modality were input into a pre-trained tumor segmentation model to delineate the regions of tumor lesions. Then, the whole tumor regions were centrally clipped from original MRI images followed by max-min normalization. Subsequently, a deep learning-based network was designed based on a unified DenseNet structure, which extracts features through a series of dense blocks. After that, two fully connected layers were used to map the features into three glioma subtypes. During the training stage, we used the images of each modality after tumor segmentation to train the network to obtain its best accuracy on our testing set. During the inferring stage, a linear weighted module based on a decision fusion strategy was applied to assemble the predicted probabilities of the pre-trained models obtained in the training stage. Finally, the performance of our method was evaluated in terms of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc.

Results

The proposed method achieved an accuracy of 0.878, an AUC of 0.902, a sensitivity of 0.772, a specificity of 0.930, a PPV of 0.862, an NPV of 0.949, and a Cohen's Kappa of 0.773, which showed a significantly higher performance than existing state-of-the-art methods.

Conclusion

Compared with current studies, this study demonstrated the effectiveness and superiority in the overall performance of our proposed multimodal MRI image decision fusion-based network method for glioma subtype classification, which would be of enormous potential value in clinical practice.

Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches

Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L9 design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA.

Role of deep learning in brain tumor detection and classification (2015 to 2020): A review

During the last decade, computer vision and machine learning have revolutionized the world in every way possible. Deep Learning is a sub field of machine learning that has shown remarkable results in every field especially biomedical field due to its ability of handling huge amount of data. Its potential and ability have also been applied and tested in the detection of brain tumor using MRI images for effective prognosis and has shown remarkable performance. The main objective of this research work is to present a detailed critical analysis of the research and findings already done to detect and classify brain tumor through MRI images in the recent past. This analysis is specifically beneficial for the researchers who are experts of deep learning and are interested to apply their expertise for brain tumor detection and classification. As a first step, a brief review of the past research papers using Deep Learning for brain tumor classification and detection is carried out. Afterwards, a critical analysis of Deep Learning techniques proposed in these research papers (2015-2020) is being carried out in the form of a Table. Finally, the conclusion highlights the merits and demerits of deep neural networks. The results formulated in this paper will provide a thorough comparison of recent studies to the future researchers, along with the idea of the effectiveness of various deep learning approaches. We are confident that this study would greatly assist in advancement of brain tumor research.

Run length encoding based wavelet features for COVID-19 detection in X-rays

OBJECTIVES

Introduced in his paper is a novel approach for the recognition of COVID-19 cases in chest X-rays.

METHODS

The discrete Wavelet transform (DWT) is employed in the proposed system to obtain highly discriminative features from the input chest X-ray image. The selected features are then classified by a support vector machine (SVM) classifier as either normal or COVID-19 cases. The DWT is well-known for its energy compression power. The proposed system uses the DWT to decompose the chest X-ray image into a group of approximation coefficients that contain a small number of high-energy (high-magnitude) coefficients. The proposed system introduces a novel coefficient selection scheme that employs hard thresholding combined with run-length encoding to extract only high-magnitude Wavelet approximation coefficients. These coefficients are utilized as features symbolizing the chest X-ray input image. After applying zero-padding to unify their lengths, the feature vectors are introduced to a SVM which classifies them as either normal or COVID-19 cases.

RESULTS

The proposed system yields promising results in terms of classification accuracy, which justifies further work in this direction.

CONCLUSION

The DWT can produce a few features that are highly discriminative. By reducing the dimensionality of the feature space, the proposed system is able to reduce the number of required training images and diminish the space and time complexities of the system.

ADVANCES IN KNOWLEDGE

Exploiting and reshaping the approximation coefficients can produce discriminative features representing the input image.