This document summarizes a research paper on using deep learning techniques to detect brain tumors in MRI images. The researchers used a dataset of 253 MRI images, with 155 containing tumors and 98 normal images. They applied convolutional neural network models like VGG-16, ResNet-50 and Inception v3 to classify images as either containing a tumor or being normal. Edge detection was used as a pre-processing step before classification. The models were trained on part of the dataset and validated using cross-validation, with final evaluation on the test set. Results showed the deep learning techniques provided accurate and reliable tumor detection, outperforming manual detection by doctors.
A Review Paper on Automated Brain Tumor DetectionIRJET Journal
This document reviews several studies that used deep learning techniques to detect brain tumors using MRI images. It summarizes key papers that used algorithms like VGG-16, ResNet, Inception, Fast R-CNN and more. VGG-16 achieved accuracies of 75.18-89.45% for detecting glioma, meningioma and pituitary tumors. ResNet-50 and Inception-V3 also performed well with 96% accuracy. Later papers compared methods like AlexNet, GoogleNet and ResNet, with VGG-16 achieving the highest accuracy of 98.69%. More recent multi-channel approaches using DenseNet201, ResNet-50 and SRN obtained 98.31% accuracy. Overall, deep learning has shown good results for brain
IRJET- Analysis of Brain Tumor Classification by using Multiple Clustering Al...IRJET Journal
This document analyzes and compares multiple clustering algorithms for brain tumor classification using MRI and PET images. It first discusses using Gray Level Co-occurrence Matrix (GLCM) to extract texture features from the images. It then analyzes the performance of k-means clustering, fuzzy c-means, Gustafson-Kessel algorithm, and density-based spectral clustering for tumor detection. The Gustafson-Kessel algorithm was found to be the most efficient based on performance.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
Survey on “Brain Tumor Detection Using Deep LearningIRJET Journal
This document summarizes a research paper on detecting brain tumors using deep learning techniques. It discusses how convolutional neural networks (CNNs) can be applied to MRI images to detect the presence of brain tumors and classify their types. The paper reviews previous work on brain tumor detection using traditional image processing and machine learning methods. It then describes the methodology used in the proposed research, which involves preprocessing MRI images, extracting features using CNN layers, and classifying tumors. The architecture of the proposed CNN model and the various modules in the brain tumor detection system are outlined. The conclusions discuss the role of image segmentation and data augmentation in medical image analysis for brain tumor detection.
Brain Tumor Detection From MRI Image Using Deep LearningIRJET Journal
This document presents a study on using deep learning techniques for brain tumor detection from MRI images. It proposes two Convolutional Neural Network models - one without transfer learning that achieves 81.42% accuracy, and one with transfer learning using the VGG16 architecture that achieves significantly higher accuracy of 98.8%. The study uses a dataset of over 5,000 MRI images categorized as normal, benign tumor, or malignant tumor. Data preprocessing techniques like filtering and enhancement are applied before training the models. Transfer learning helps reduce training time and improves model performance for tumor classification compared to training from scratch without transferring learned features.
Brain Tumor Detection and Segmentation using UNETIRJET Journal
This document discusses brain tumor detection and segmentation using the UNET model. It analyzes previous research on brain tumor segmentation techniques and their limitations. The proposed method uses the BraTS 2020 dataset containing 369 MRI images for training and 125 for testing. It develops a 3D UNET model for multimodal brain tumor segmentation. The model generates 3D outputs and achieves 98.5% accuracy in segmenting whole, core and enhancing tumors.
A Review Paper on Automated Brain Tumor DetectionIRJET Journal
This document reviews several studies that used deep learning techniques to detect brain tumors using MRI images. It summarizes key papers that used algorithms like VGG-16, ResNet, Inception, Fast R-CNN and more. VGG-16 achieved accuracies of 75.18-89.45% for detecting glioma, meningioma and pituitary tumors. ResNet-50 and Inception-V3 also performed well with 96% accuracy. Later papers compared methods like AlexNet, GoogleNet and ResNet, with VGG-16 achieving the highest accuracy of 98.69%. More recent multi-channel approaches using DenseNet201, ResNet-50 and SRN obtained 98.31% accuracy. Overall, deep learning has shown good results for brain
IRJET- Analysis of Brain Tumor Classification by using Multiple Clustering Al...IRJET Journal
This document analyzes and compares multiple clustering algorithms for brain tumor classification using MRI and PET images. It first discusses using Gray Level Co-occurrence Matrix (GLCM) to extract texture features from the images. It then analyzes the performance of k-means clustering, fuzzy c-means, Gustafson-Kessel algorithm, and density-based spectral clustering for tumor detection. The Gustafson-Kessel algorithm was found to be the most efficient based on performance.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
Survey on “Brain Tumor Detection Using Deep LearningIRJET Journal
This document summarizes a research paper on detecting brain tumors using deep learning techniques. It discusses how convolutional neural networks (CNNs) can be applied to MRI images to detect the presence of brain tumors and classify their types. The paper reviews previous work on brain tumor detection using traditional image processing and machine learning methods. It then describes the methodology used in the proposed research, which involves preprocessing MRI images, extracting features using CNN layers, and classifying tumors. The architecture of the proposed CNN model and the various modules in the brain tumor detection system are outlined. The conclusions discuss the role of image segmentation and data augmentation in medical image analysis for brain tumor detection.
Brain Tumor Detection From MRI Image Using Deep LearningIRJET Journal
This document presents a study on using deep learning techniques for brain tumor detection from MRI images. It proposes two Convolutional Neural Network models - one without transfer learning that achieves 81.42% accuracy, and one with transfer learning using the VGG16 architecture that achieves significantly higher accuracy of 98.8%. The study uses a dataset of over 5,000 MRI images categorized as normal, benign tumor, or malignant tumor. Data preprocessing techniques like filtering and enhancement are applied before training the models. Transfer learning helps reduce training time and improves model performance for tumor classification compared to training from scratch without transferring learned features.
Brain Tumor Detection and Segmentation using UNETIRJET Journal
This document discusses brain tumor detection and segmentation using the UNET model. It analyzes previous research on brain tumor segmentation techniques and their limitations. The proposed method uses the BraTS 2020 dataset containing 369 MRI images for training and 125 for testing. It develops a 3D UNET model for multimodal brain tumor segmentation. The model generates 3D outputs and achieves 98.5% accuracy in segmenting whole, core and enhancing tumors.
IRJET- Image Classification using Deep Learning Neural Networks for Brain...IRJET Journal
This document discusses using a convolutional neural network (CNN) to classify brain tumor MRI images. It begins with an introduction to brain tumors and MRI as a diagnostic tool. It then reviews related work applying deep learning to medical image classification tasks. The proposed CNN model contains convolutional and max pooling layers for feature extraction, and fully connected layers for classification. The model is trained on a dataset of 253 MRI brain images from Kaggle to classify images as containing a tumor or being tumor-free. Experimental results show the CNN achieving 98.5% accuracy in classification, demonstrating the feasibility of the approach.
IRJET- Brain Tumor Detection and Classification with Feed Forward Back Propag...IRJET Journal
This document presents a method for detecting and classifying brain tumors in MRI images using a feed forward back propagation neural network. It first preprocesses MRI images by dividing them into blocks and applying Haar transforms for noise removal and edge preservation. Statistical, GLCM, morphological and edge features are then extracted from each block. These features are used to identify abnormal areas. The blocks are then classified as normal or tumor using a feed forward back propagation neural network, which can model nonlinear relationships and is trained to reduce error rates. The method achieves 98% classification accuracy on a benchmark MRI dataset. It results in high accuracy tumor detection with less iterations, reducing computation time compared to previous methods.
IRJET - Machine Learning Applications on Cancer Prognosis and PredictionIRJET Journal
This document discusses machine learning applications for cancer prognosis and prediction using MRI images. It presents a methodology for detecting brain tumors from MRI reports using image segmentation in MATLAB. The key steps include pre-processing MRI images, segmenting the tumor area using algorithms like fuzzy C-means and watershed, extracting features from the tumor region, and classifying tumors as benign or malignant. The proposed system achieved encouraging results for accuracy and precision in automatic brain tumor detection and classification. Future work may involve classifying tumor types and monitoring tumor growth over time using sequential patient images.
The document discusses using a U-Net convolutional neural network to automatically segment brain tumors in MRI images. It aims to eliminate the need for domain expertise by using deep learning to extract hierarchical features. The U-Net model is trained on the BRATS 2017 dataset and is able to segment tumors with 5% higher accuracy than previous methods, as measured by the Dice similarity coefficient. The system could be expanded to analyze additional MRI modalities and further improve automated tumor detection.
The document discusses using a U-Net convolutional neural network to automatically segment brain tumors in MRI images. It aims to eliminate the need for domain expertise by using deep learning to extract hierarchical features. The U-Net model is trained on the BRATS 2017 dataset and is able to segment tumors with 5% higher accuracy than previous methods, as measured by the Dice similarity coefficient. The system could be expanded to analyze additional MRI modalities and further improve automated tumor detection.
Brain Tumor Detection and Classification Using MRI Brain ImagesIRJET Journal
This document presents research on detecting and classifying brain tumors using MRI images. It discusses:
1) Using k-means clustering for pre-processing MRI images to reduce noise and increase detection accuracy. Marker-controlled watershed transformation and grey-level co-occurrence matrix are then used for tumor detection and feature extraction.
2) Two classification methods are employed: support vector machine (SVM) and artificial neural network (ANN). SVM and ANN have been shown to accurately classify tumors in an effective manner.
3) The paper proposes an algorithm to differentiate between benign and malignant tumors using watershed segmentation and extracting grey-level co-occurrence matrix features from MRI images, which are then classified using SVM and AN
AI Based Approach for Classification of MultiGrade Tumour in Human BrainIRJET Journal
This document presents a study on developing an AI-based method for classifying multigrade brain tumors using MRI images. The study uses datasets of MRI images to train and test models like U-Net, VGG-16, AlexNet and ResNet50. For smaller datasets, AlexNet achieved 99% accuracy for normal classification and 77.5% average IOU for multigrade classification. The study proposes a methodology including data collection, preprocessing, classification training and model testing. Different models are experimented with including U-Net, VGG-16 and AlexNet on small and medium datasets. For medium datasets, U-Net achieved 74% accuracy, VGG-16 achieved 66% accuracy and AlexNet achieved 99% accuracy. The
This document presents a model to detect and classify brain tumors using watershed algorithm for image segmentation and convolutional neural networks (CNN). The model takes MRI images as input, pre-processes the images by converting them to grayscale and removing noise, then uses watershed algorithm for image segmentation and CNN for tumor classification. The CNN architecture achieves classification of three tumor types. Previous related works that also used deep learning methods for brain tumor detection and classification are discussed. The proposed system methodology involves inputting MRI images, pre-processing, segmentation using watershed algorithm, and classification of tumorous vs non-tumorous cells using CNN.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
BRAIN TUMOR DETECTION USING CNN & ML TECHNIQUESIRJET Journal
1) The document proposes two methods for detecting brain tumors using MRI images - one using traditional machine learning classifiers after segmentation with FCM and feature extraction, and one using a convolutional neural network.
2) For the first method, MRI images undergo preprocessing like skull stripping and noise removal before segmentation with Fuzzy C-Means clustering and morphological operations. Features are then extracted and classified with models like KNN, logistic regression, random forest.
3) For the second method, a 5-layer CNN is used to directly classify tumor images. The CNN includes convolutional, max pooling, flatten, and dense layers to reduce parameters and detect tumors with 92.42% accuracy.
IRJET - Brain Tumor Detection using Image Processing, ML & NLPIRJET Journal
This document presents a system for detecting brain tumors using image processing, machine learning, and natural language processing. The system applies preprocessing, filtering, and segmentation techniques to MRI images to extract features of the tumor such as shape, size, texture, and contrast. Machine learning algorithms are then used to classify tumors and detect their location. The system aims to make tumor detection more efficient and accurate compared to manual detection. It evaluates performance based on metrics like accuracy, sensitivity, specificity, and dice coefficient. The authors conclude the proposed approach can help timely and precise tumor detection and localization.
IRJET- Brain Tumor Detection using Image Processing, ML & NLPIRJET Journal
This document presents a system for detecting brain tumors using image processing, machine learning, and natural language processing. The system applies preprocessing, filtering, and segmentation techniques to MRI images to extract features of the tumor such as shape, size, texture, and contrast. Machine learning algorithms are then used to classify tumors and detect their location. The system aims to make tumor detection more efficient and accurate compared to manual detection. It evaluates performance based on metrics like accuracy, sensitivity, specificity, and dice coefficient. The authors conclude the proposed approach can help timely and precise tumor detection and localization.
IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and ThresholdingIRJET Journal
The document presents a new hybrid model for detecting brain tumors in MRI images. It uses a combination of discrete cosine transform (DCT), discrete wavelet transform (DWT), principal component analysis (PCA), and fuzzy c-means clustering. DCT and DWT are applied to extract features from MRI images. PCA is then used to reduce the dimensions of the extracted features. Finally, fuzzy c-means clustering is used to segment and detect tumors. The proposed hybrid model is evaluated using objective metrics like RMSE, PSNR, correlation, contrast and entropy. Results show the hybrid model achieves better values for these metrics compared to using DCT or DWT alone, indicating it more accurately detects and segments tumors in MRI images.
IRJET- Diversified Segmentation and Classification Techniques on Brain Tu...IRJET Journal
This document summarizes 20 research papers on techniques for detecting brain tumors using medical images like MRI scans. It discusses several techniques for image segmentation, feature extraction, and classification that have been used to automatically detect and diagnose brain tumors. The goal of the work is to consolidate these different techniques and provide new insights on recent approaches to brain tumor image processing. Key methods discussed include convolutional neural networks, random forest classifiers, discrete wavelet transforms, and probabilistic neural networks.
IRJET - Detection of Heamorrhage in Brain using Deep LearningIRJET Journal
This document presents a method for detecting hemorrhage in brain CT scans using deep learning. It begins with an introduction to brain hemorrhage and the need for automated detection. Previous related work using various segmentation and classification methods is summarized. Deep learning is identified as a promising technique due to its ability to extract complex features from images. The proposed method uses a convolutional neural network model with several convolutional, max pooling, dropout and dense layers to classify brain CT scans as either normal or hemorrhagic. The model is trained on 180 images and tested on 20 images, achieving an accuracy of 94.4% at predicting hemorrhage. The method provides a fast and automated way to detect hemorrhage in brain CT scans to help
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
IRJET - An Efficient Approach for Multi-Modal Brain Tumor Classification usin...IRJET Journal
This paper proposes an efficient approach for multi-modal brain tumor classification using texture features and machine learning. It uses the MICCAI BraTS 2016 dataset and segments tumors using fuzzy c-means clustering. It then extracts texture features like GLCM and LBP and classifies tumors as benign or malignant using an SVM classifier. The proposed method achieved accurate segmentation and classification of brain tumors in MRI images.
Automated Intracranial Neoplasm Detection Using Convolutional Neural NetworksIRJET Journal
This document presents a study that uses convolutional neural networks to automatically detect intracranial neoplasms (brain tumors) from MRI scans. The researchers developed a CNN model that achieved 97.87% accuracy in identifying tumors. They used preprocessed MRI images to train and test the model for tumor detection. Convolutional neural networks are a type of deep learning that can provide efficient results for medical image classification tasks like tumor detection compared to traditional methods. The study demonstrates that CNNs are a promising approach for automated brain tumor identification from MRI scans.
Mri brain image segmentatin and classification by modified fcm &svm akorithmeSAT Journals
Abstract Brain Tumor detection is challenging task in biomedical field. Image segmentation is a key step from the image processing to image analysis, it occupy an important place. The manual segmentation of brain image is challenging and time consuming task. An automated system overcomes the drawbacks as well as it segments the white matter, grey matter, cerebrospinal fluid and edema. This clustering approach is particularly used for brain tumor detection in abnormal MR images. In this paper the application of Modified FCM algorithm for Brain tumor detection and its classification by SVM algorithm is focused. The Magnetic Resonance image is converted in to vector format and that is given as input to the modified fuzzy c-means algorithm. In modified fuzzy c-means the steps are: initial fuzzy partitioning and fuzzy membership generation Cluster updation based on objective function, Assigning labels to pixels of each category and display segmented image that will give more meaningful regions to analyze. This clustered images served as inputs to SVM. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes. Keywords: Clustering, Classification, Fuzz C-Means, Support Vector Machine, MRI, Brain Tumor.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
More Related Content
Similar to Brain Tumor Detection Using Deep Learning
IRJET- Image Classification using Deep Learning Neural Networks for Brain...IRJET Journal
This document discusses using a convolutional neural network (CNN) to classify brain tumor MRI images. It begins with an introduction to brain tumors and MRI as a diagnostic tool. It then reviews related work applying deep learning to medical image classification tasks. The proposed CNN model contains convolutional and max pooling layers for feature extraction, and fully connected layers for classification. The model is trained on a dataset of 253 MRI brain images from Kaggle to classify images as containing a tumor or being tumor-free. Experimental results show the CNN achieving 98.5% accuracy in classification, demonstrating the feasibility of the approach.
IRJET- Brain Tumor Detection and Classification with Feed Forward Back Propag...IRJET Journal
This document presents a method for detecting and classifying brain tumors in MRI images using a feed forward back propagation neural network. It first preprocesses MRI images by dividing them into blocks and applying Haar transforms for noise removal and edge preservation. Statistical, GLCM, morphological and edge features are then extracted from each block. These features are used to identify abnormal areas. The blocks are then classified as normal or tumor using a feed forward back propagation neural network, which can model nonlinear relationships and is trained to reduce error rates. The method achieves 98% classification accuracy on a benchmark MRI dataset. It results in high accuracy tumor detection with less iterations, reducing computation time compared to previous methods.
IRJET - Machine Learning Applications on Cancer Prognosis and PredictionIRJET Journal
This document discusses machine learning applications for cancer prognosis and prediction using MRI images. It presents a methodology for detecting brain tumors from MRI reports using image segmentation in MATLAB. The key steps include pre-processing MRI images, segmenting the tumor area using algorithms like fuzzy C-means and watershed, extracting features from the tumor region, and classifying tumors as benign or malignant. The proposed system achieved encouraging results for accuracy and precision in automatic brain tumor detection and classification. Future work may involve classifying tumor types and monitoring tumor growth over time using sequential patient images.
The document discusses using a U-Net convolutional neural network to automatically segment brain tumors in MRI images. It aims to eliminate the need for domain expertise by using deep learning to extract hierarchical features. The U-Net model is trained on the BRATS 2017 dataset and is able to segment tumors with 5% higher accuracy than previous methods, as measured by the Dice similarity coefficient. The system could be expanded to analyze additional MRI modalities and further improve automated tumor detection.
The document discusses using a U-Net convolutional neural network to automatically segment brain tumors in MRI images. It aims to eliminate the need for domain expertise by using deep learning to extract hierarchical features. The U-Net model is trained on the BRATS 2017 dataset and is able to segment tumors with 5% higher accuracy than previous methods, as measured by the Dice similarity coefficient. The system could be expanded to analyze additional MRI modalities and further improve automated tumor detection.
Brain Tumor Detection and Classification Using MRI Brain ImagesIRJET Journal
This document presents research on detecting and classifying brain tumors using MRI images. It discusses:
1) Using k-means clustering for pre-processing MRI images to reduce noise and increase detection accuracy. Marker-controlled watershed transformation and grey-level co-occurrence matrix are then used for tumor detection and feature extraction.
2) Two classification methods are employed: support vector machine (SVM) and artificial neural network (ANN). SVM and ANN have been shown to accurately classify tumors in an effective manner.
3) The paper proposes an algorithm to differentiate between benign and malignant tumors using watershed segmentation and extracting grey-level co-occurrence matrix features from MRI images, which are then classified using SVM and AN
AI Based Approach for Classification of MultiGrade Tumour in Human BrainIRJET Journal
This document presents a study on developing an AI-based method for classifying multigrade brain tumors using MRI images. The study uses datasets of MRI images to train and test models like U-Net, VGG-16, AlexNet and ResNet50. For smaller datasets, AlexNet achieved 99% accuracy for normal classification and 77.5% average IOU for multigrade classification. The study proposes a methodology including data collection, preprocessing, classification training and model testing. Different models are experimented with including U-Net, VGG-16 and AlexNet on small and medium datasets. For medium datasets, U-Net achieved 74% accuracy, VGG-16 achieved 66% accuracy and AlexNet achieved 99% accuracy. The
This document presents a model to detect and classify brain tumors using watershed algorithm for image segmentation and convolutional neural networks (CNN). The model takes MRI images as input, pre-processes the images by converting them to grayscale and removing noise, then uses watershed algorithm for image segmentation and CNN for tumor classification. The CNN architecture achieves classification of three tumor types. Previous related works that also used deep learning methods for brain tumor detection and classification are discussed. The proposed system methodology involves inputting MRI images, pre-processing, segmentation using watershed algorithm, and classification of tumorous vs non-tumorous cells using CNN.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
BRAIN TUMOR DETECTION USING CNN & ML TECHNIQUESIRJET Journal
1) The document proposes two methods for detecting brain tumors using MRI images - one using traditional machine learning classifiers after segmentation with FCM and feature extraction, and one using a convolutional neural network.
2) For the first method, MRI images undergo preprocessing like skull stripping and noise removal before segmentation with Fuzzy C-Means clustering and morphological operations. Features are then extracted and classified with models like KNN, logistic regression, random forest.
3) For the second method, a 5-layer CNN is used to directly classify tumor images. The CNN includes convolutional, max pooling, flatten, and dense layers to reduce parameters and detect tumors with 92.42% accuracy.
IRJET - Brain Tumor Detection using Image Processing, ML & NLPIRJET Journal
This document presents a system for detecting brain tumors using image processing, machine learning, and natural language processing. The system applies preprocessing, filtering, and segmentation techniques to MRI images to extract features of the tumor such as shape, size, texture, and contrast. Machine learning algorithms are then used to classify tumors and detect their location. The system aims to make tumor detection more efficient and accurate compared to manual detection. It evaluates performance based on metrics like accuracy, sensitivity, specificity, and dice coefficient. The authors conclude the proposed approach can help timely and precise tumor detection and localization.
IRJET- Brain Tumor Detection using Image Processing, ML & NLPIRJET Journal
This document presents a system for detecting brain tumors using image processing, machine learning, and natural language processing. The system applies preprocessing, filtering, and segmentation techniques to MRI images to extract features of the tumor such as shape, size, texture, and contrast. Machine learning algorithms are then used to classify tumors and detect their location. The system aims to make tumor detection more efficient and accurate compared to manual detection. It evaluates performance based on metrics like accuracy, sensitivity, specificity, and dice coefficient. The authors conclude the proposed approach can help timely and precise tumor detection and localization.
IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and ThresholdingIRJET Journal
The document presents a new hybrid model for detecting brain tumors in MRI images. It uses a combination of discrete cosine transform (DCT), discrete wavelet transform (DWT), principal component analysis (PCA), and fuzzy c-means clustering. DCT and DWT are applied to extract features from MRI images. PCA is then used to reduce the dimensions of the extracted features. Finally, fuzzy c-means clustering is used to segment and detect tumors. The proposed hybrid model is evaluated using objective metrics like RMSE, PSNR, correlation, contrast and entropy. Results show the hybrid model achieves better values for these metrics compared to using DCT or DWT alone, indicating it more accurately detects and segments tumors in MRI images.
IRJET- Diversified Segmentation and Classification Techniques on Brain Tu...IRJET Journal
This document summarizes 20 research papers on techniques for detecting brain tumors using medical images like MRI scans. It discusses several techniques for image segmentation, feature extraction, and classification that have been used to automatically detect and diagnose brain tumors. The goal of the work is to consolidate these different techniques and provide new insights on recent approaches to brain tumor image processing. Key methods discussed include convolutional neural networks, random forest classifiers, discrete wavelet transforms, and probabilistic neural networks.
IRJET - Detection of Heamorrhage in Brain using Deep LearningIRJET Journal
This document presents a method for detecting hemorrhage in brain CT scans using deep learning. It begins with an introduction to brain hemorrhage and the need for automated detection. Previous related work using various segmentation and classification methods is summarized. Deep learning is identified as a promising technique due to its ability to extract complex features from images. The proposed method uses a convolutional neural network model with several convolutional, max pooling, dropout and dense layers to classify brain CT scans as either normal or hemorrhagic. The model is trained on 180 images and tested on 20 images, achieving an accuracy of 94.4% at predicting hemorrhage. The method provides a fast and automated way to detect hemorrhage in brain CT scans to help
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
IRJET - An Efficient Approach for Multi-Modal Brain Tumor Classification usin...IRJET Journal
This paper proposes an efficient approach for multi-modal brain tumor classification using texture features and machine learning. It uses the MICCAI BraTS 2016 dataset and segments tumors using fuzzy c-means clustering. It then extracts texture features like GLCM and LBP and classifies tumors as benign or malignant using an SVM classifier. The proposed method achieved accurate segmentation and classification of brain tumors in MRI images.
Automated Intracranial Neoplasm Detection Using Convolutional Neural NetworksIRJET Journal
This document presents a study that uses convolutional neural networks to automatically detect intracranial neoplasms (brain tumors) from MRI scans. The researchers developed a CNN model that achieved 97.87% accuracy in identifying tumors. They used preprocessed MRI images to train and test the model for tumor detection. Convolutional neural networks are a type of deep learning that can provide efficient results for medical image classification tasks like tumor detection compared to traditional methods. The study demonstrates that CNNs are a promising approach for automated brain tumor identification from MRI scans.
Mri brain image segmentatin and classification by modified fcm &svm akorithmeSAT Journals
Abstract Brain Tumor detection is challenging task in biomedical field. Image segmentation is a key step from the image processing to image analysis, it occupy an important place. The manual segmentation of brain image is challenging and time consuming task. An automated system overcomes the drawbacks as well as it segments the white matter, grey matter, cerebrospinal fluid and edema. This clustering approach is particularly used for brain tumor detection in abnormal MR images. In this paper the application of Modified FCM algorithm for Brain tumor detection and its classification by SVM algorithm is focused. The Magnetic Resonance image is converted in to vector format and that is given as input to the modified fuzzy c-means algorithm. In modified fuzzy c-means the steps are: initial fuzzy partitioning and fuzzy membership generation Cluster updation based on objective function, Assigning labels to pixels of each category and display segmented image that will give more meaningful regions to analyze. This clustered images served as inputs to SVM. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes. Keywords: Clustering, Classification, Fuzz C-Means, Support Vector Machine, MRI, Brain Tumor.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to Brain Tumor Detection Using Deep Learning (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia