Brain tumor multi classification and segmentation in MRI images using deep learning

• URL: http://arxiv.org/abs/2304.10039v2
• Date: Fri, 23 Jun 2023 21:29:42 GMT
• Title: Brain tumor multi classification and segmentation in MRI images using deep learning
• Authors: Belal Amin, Romario Sameh Samir, Youssef Tarek, Mohammed Ahmed, Rana Ibrahim, Manar Ahmed, Mohamed Hassan
• Abstract summary: The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images.
• Score: 3.1248717814228923
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images. The models are evaluated on a publicly available dataset and achieve high accuracy and segmentation metrics, indicating their potential for clinical use in the diagnosis and treatment of brain tumors.

Related papers

• Machine learning approach to brain tumor detection and classification [11.108853789803597]
  We apply various statistical and machine learning models to detect and classify brain tumors using brain MRI images. Our findings show that CNN outperforms other models, achieving the best performance. This study demonstrates that machine learning approaches are suitable for brain tumor detection and classification, facilitating real-world medical applications.
  arXiv  Detail & Related papers  (2024-10-16T15:52:32Z)
• Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation [48.107348956719775]
  We introduce Mask-Enhanced SAM (M-SAM), an innovative architecture tailored for 3D tumor lesion segmentation. We propose a novel Mask-Enhanced Adapter (MEA) within M-SAM that enriches the semantic information of medical images with positional data from coarse segmentation masks. Our M-SAM achieves high segmentation accuracy and also exhibits robust generalization.
  arXiv  Detail & Related papers  (2024-03-09T13:37:02Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images [0.15833270109954137]
  The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically designed for medical image segmentation. The pretrained SAM architecture incorporates a mechanism to capture spatial dependencies and generate segmentation results.
  arXiv  Detail & Related papers  (2023-06-21T18:49:21Z)
• Brain Tumor Segmentation from MRI Images using Deep Learning Techniques [3.1498833540989413]
  A public MRI dataset contains 3064 TI-weighted images from 233 patients with three variants of brain tumor, viz. meningioma, glioma, and pituitary tumor. The dataset files were converted and preprocessed before indulging into the methodology which employs implementation and training of some well-known image segmentation deep learning models. The experimental findings showed that among all the applied approaches, the recurrent residual U-Net which uses Adam reaches a Mean Intersection Over Union of 0.8665 and outperforms other compared state-of-the-art deep learning models.
  arXiv  Detail & Related papers  (2023-04-29T13:33:21Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation [12.256043883052506]
  Brain tumor analysis in MRI images is a significant and challenging issue because misdiagnosis can lead to death. This paper presents an approach that simultaneously segments and classifies brain tumors in MRI images using a framework that contains MRI image enhancement and tumor region detection. Subjective and objective results indicate that the segmentation and classification results based on evaluation metrics are better or comparable to the state-of-the-art.
  arXiv  Detail & Related papers  (2021-12-28T22:49:44Z)
• Triplet Contrastive Learning for Brain Tumor Classification [99.07846518148494]
  We present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare.
  arXiv  Detail & Related papers  (2021-08-08T11:26:34Z)
• MAG-Net: Mutli-task attention guided network for brain tumor segmentation and classification [0.9176056742068814]
  This paper proposes multi-task attention guided encoder-decoder network (MAG-Net) to classify and segment the brain tumor regions using MRI images. The model achieved promising results as compared to existing state-of-the-art models.
  arXiv  Detail & Related papers  (2021-07-26T16:51:00Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.