Robust Brain MRI Image Classification with SIBOW-SVM

• URL: http://arxiv.org/abs/2311.08908v1
• Date: Wed, 15 Nov 2023 12:26:24 GMT
• Title: Robust Brain MRI Image Classification with SIBOW-SVM
• Authors: Liyun Zeng and Hao Helen Zhang
• Abstract summary: Early detection of brain tumor types is critical for cancer prevention and treatment, ultimately improving human life expectancy. MRI stands as the most effective technique to detect brain tumors by generating comprehensive brain images through scans. Deep learning-based image classification methods, including CNN, face challenges in estimating class probabilities without proper model calibration. We propose a novel brain tumor image classification method, called SIBOW-SVM, which integrates the Bag-of-Features (BoF) model with SIFT feature extraction and weighted Support Vector Machines (wSVMs) Our results show that the new method outperforms state-of-the-art
• Score: 1.3597551064547502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The majority of primary Central Nervous System (CNS) tumors in the brain are among the most aggressive diseases affecting humans. Early detection of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) stands as the most effective technique to detect brain tumors by generating comprehensive brain images through scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning (ML) methods, such as Convolutional Neural Network (CNN), have demonstrated significantly higher accuracy than manual screening, while maintaining low computational costs. Nonetheless, deep learning-based image classification methods, including CNN, face challenges in estimating class probabilities without proper model calibration. In this paper, we propose a novel brain tumor image classification method, called SIBOW-SVM, which integrates the Bag-of-Features (BoF) model with SIFT feature extraction and weighted Support Vector Machines (wSVMs). This new approach effectively captures hidden image features, enabling the differentiation of various tumor types and accurate label predictions. Additionally, the SIBOW-SVM is able to estimate the probabilities of images belonging to each class, thereby providing high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive images. As a benchmark, we apply the SIBOW-SVM to a public data set of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results show that the new method outperforms state-of-the-art methods, including CNN.

Related papers

• Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches [0.0]
  Brain tumors pose a serious health threat due to their rapid growth and potential for metastasis. This study aims to improve the efficiency and accuracy of brain tumor classification. Our approach combines state-of-the-art deep learning algorithms, including the Vision Transformer (ViT), Capsule Neural Network (CapsNet), and convolutional neural networks (CNNs) such as ResNet-152 and VGG16.
  arXiv  Detail & Related papers  (2024-10-31T07:28:06Z)
• 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)
• Brain Tumor Classification From MRI Images Using Machine Learning [0.24739484546803336]
  Brain tumor is a life-threatening problem and hampers the normal functioning of the human body. The use of deep learning algorithms in medical imaging has resulted in significant improvements in the classification and diagnosis of brain tumors. The objective of this project is to develop a predictive system for brain tumor detection using machine learning.
  arXiv  Detail & Related papers  (2024-07-15T11:30:40Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• 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)
• 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)
• 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)
• 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)
• 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)
• 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.