2,118 research outputs found
Brain Tumor Detection and Multi Classification Using GNB-Based Machine Learning Approach
In an abnormal tissue called a brain tumor, the cells of the tumor reproduce quickly. if no control over tumor cell growth. The difficulties involved in identifying and treating brain tumors Machine learning is the most technologically sophisticated tool for classification and detection, implementing reliable state-of-the-art A.I. as well as neural network classification techniques, the use of this technology in early diagnosis detection of brain tumors can be accomplished successfully. it is well known that the segmentation method is capable of helping simply destroy the brain's abnormal tumor regions In order to segment and categorize brain tumors, this study suggests a multimodal approach involving machine learning and medical assistance. Noise can be seen in MRI images. To make the method for eliminating noise from images easier, a geometric mean is used later. The algorithms used to segment an image into smaller pieces are fuzzy c-means algorithms. Detection of a specific area of interest is made simpler by segmentation. The dimension reduction procedure is carried out using the GLCM. Photographic features are extracted using the GLCM algorithm. Then, using a variety of ML techniques, like as CNN, ANN, SVM, Gaussian NB, and Adaptive Boosting, the photos are categorized. The Gaussian NB method performs more effectively with regard to the identification and classification of brain tumors. The plasterwork work achieved 98.80 percent accuracy using GNB, RBF SVM
Efficient segmentation and classification of the tumor using improved encoder-decoder architecture in brain MRI images
Primary diagnosis of brain tumors is crucial to improve treatment outcomes for patient survival. T1-weighted contrast-enhanced images of Magnetic Resonance Imaging (MRI) provide the most anatomically relevant images. But even with many advancements, day by day in the medical field, assessing tumor shape, size, segmentation, and classification is very difficult as manual segmentation of MRI images with high precision and accuracy is indeed a time-consuming and very challenging task. So newer digital methods like deep learning algorithms are used for tumor diagnosis which may lead to far better results. Deep learning algorithms have significantly upgraded the research in the artificial intelligence field and help in better understanding medical images and their further analysis. The work carried out in this paper presents a fully automatic brain tumor segmentation and classification model with encoder-decoder architecture that is an improvisation of traditional UNet architecture achieved by embedding three variants of ResNet like ResNet 50, ResNet 101, and ResNext 50 with proper hyperparameter tuning. Various data augmentation techniques were used to improve the model performance. The overall performance of the model was tested on a publicly available MRI image dataset containing three common types of tumors. The proposed model performed better in comparison to several other deep learning architectures regarding quality parameters including Dice Similarity Coefficient (DSC) and Mean Intersection over Union (Mean IoU) thereby enhancing the tumor analysis
Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images
Diagnosing Brain Tumor with the aid of Magnetic Resonance Imaging (MRI) has
gained enormous prominence over the years, primarily in the field of medical
science. Detection and/or partitioning of brain tumors solely with the aid of
MR imaging is achieved at the cost of immense time and effort and demands a lot
of expertise from engaged personnel. This substantiates the necessity of
fabricating an autonomous model brain tumor diagnosis. Our work involves
implementing a deep convolutional neural network (DCNN) for diagnosing brain
tumors from MR images. The dataset used in this paper consists of 253 brain MR
images where 155 images are reported to have tumors. Our model can single out
the MR images with tumors with an overall accuracy of 96%. The model
outperformed the existing conventional methods for the diagnosis of brain tumor
in the test dataset (Precision = 0.93, Sensitivity = 1.00, and F1-score =
0.97). Moreover, the proposed model's average precision-recall score is 0.93,
Cohen's Kappa 0.91, and AUC 0.95. Therefore, the proposed model can help
clinical experts verify whether the patient has a brain tumor and,
consequently, accelerate the treatment procedure.Comment: 4th International conference on I-SMAC (IoT in Social, Mobile,
Analytics and Cloud) (I-SMAC 2020), IEEE, 7-9 October 2020, TamilNadu, INDI
Utilizing Segment Anything Model For Assessing Localization of GRAD-CAM in Medical Imaging
The introduction of saliency map algorithms as an approach for assessing the
interoperability of images has allowed for a deeper understanding of current
black-box models with Artificial Intelligence. Their rise in popularity has led
to these algorithms being applied in multiple fields, including medical
imaging. With a classification task as important as those in the medical
domain, a need for rigorous testing of their capabilities arises. Current works
examine capabilities through assessing the localization of saliency maps upon
medical abnormalities within an image, through comparisons with human
annotations. We propose utilizing Segment Anything Model (SAM) to both further
the accuracy of such existing metrics, while also generalizing beyond the need
for human annotations. Our results show both high degrees of similarity to
existing metrics while also highlighting the capabilities of this methodology
to beyond human-annotation. Furthermore, we explore the applications (and
challenges) of SAM within the medical domain, including image pre-processing
before segmenting, natural language proposals to SAM in the form of CLIP-SAM,
and SAM accuracy across multiple medical imaging datasets.Comment: 11 pages, 14 figures, 1 tabl
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