Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images

• URL: http://arxiv.org/abs/2010.11978v1
• Date: Sat, 3 Oct 2020 07:42:17 GMT
• Title: Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images
• Authors: Md. Abu Bakr Siddique, Shadman Sakib, Mohammad Mahmudur Rahman Khan, Abyaz Kader Tanzeem, Madiha Chowdhury, Nowrin Yasmin
• Abstract summary: Our work involves implementing a deep convolutional neural network (DCNN) for diagnosing brain tumors from MR images. Our model can single out the MR images with tumors with an overall accuracy of 96%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images [0.0]
  Brain tumors pose a significant global health challenge due to their high prevalence and mortality rates across all age groups. This study presents a comprehensive investigation into the use of Convolutional Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy individuals and patients with brain tumors, was processed and fed into the CNN architecture.
  arXiv  Detail & Related papers  (2023-10-26T18:27:20Z)
• Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning [55.789874096142285]
  We present a deep learning-based brain tumor recurrence location prediction network. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location.
  arXiv  Detail & Related papers  (2023-04-11T02:45:38Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection [0.0]
  This study proposes a model for the early detection of brain tumours using a combination of convolutional neural networks (CNNs) and extreme gradient boosting (XGBoost) The proposed model, named C-XGBoost has a lower model complexity compared to purely CNNs, making it easier to train and less prone to overfitting. It is also better able to handle imbalanced and unstructured data, which are common issues in real-world medical image classification tasks.
  arXiv  Detail & Related papers  (2023-01-05T22:25:28Z)
• A deep learning approach for brain tumor detection using magnetic resonance imaging [0.0]
  Brain tumors are considered one of the most dangerous disorders in children and adults. A convolution neural network (CNN)-based illustration has been proposed for detecting brain tumors from MRI images. The proposed model has achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate.
  arXiv  Detail & Related papers  (2022-10-25T10:13:29Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging [86.52489226518955]
  We extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks. Our method won the RSNA 2021 Brain Tumor AI Challenge Prize (Segmentation Task)
  arXiv  Detail & Related papers  (2022-02-10T06:34:32Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model [3.6328238032703806]
  We analyze the existing technology and propose an attention guided deep convolution neural network (CNN) model. Our method can achieve the average accuracy of 99.18% under ten-fold cross-validation for identifying the presence or absence of tumor. It can assist doctors in achieving efficient clinical diagnosis of brain tumors.
  arXiv  Detail & Related papers  (2021-04-06T07:25:52Z)
• Brain Tumor Classification Using Medial Residual Encoder Layers [9.038707616951795]
  Cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone. Brain tumors count for one out of every four cancer deaths. We propose a system based on deep learning, containing encoder blocks. These blocks are fed with post-max-pooling features as residual learning. Experimental evaluations of this model on a dataset consisting of 3064 MR images show 95.98% accuracy, which is better than previous studies on this database.
  arXiv  Detail & Related papers  (2020-11-01T21:19:38Z)

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.