Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features

• URL: http://arxiv.org/abs/2202.13853v1
• Date: Mon, 28 Feb 2022 15:09:42 GMT
• Title: Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features
• Authors: Rafael Rodrigues, Susana Quijano-Roy, Robert-Yves Carlier, and Antonio M. G. Pinheiro
• Abstract summary: Three methods are proposed to classify target muscles in Collagen VI-related myopathy cases. The best results were obtained with the hybrid model, resulting in a global accuracy of 93.8%.
• Score: 0.34998703934432684
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods was evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy or Bethlem Myopathy patients at different evolution stages. The best results were obtained with the hybrid model, resulting in a global accuracy of 93.8\%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.

Related papers

• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [45.630166504856255]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Classification of Radiologically Isolated Syndrome and Clinically Isolated Syndrome with Machine-Learning Techniques [0.0]
  The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS) Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS.
  arXiv  Detail & Related papers  (2024-01-24T08:49:50Z)
• Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis [38.150634108667774]
  Histologic remission is a new therapeutic target in ulcerative colitis (UC) Endocytoscopy (EC) is a novel ultra-high magnification endoscopic technique. We propose a neural network model that can assess histological disease activity in EC images.
  arXiv  Detail & Related papers  (2023-08-28T15:54:14Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus [46.1292414445895]
  Paranasal anomalies can present with a wide range of morphological features. Current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. We investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary (MS) and MS with polyps or cysts.
  arXiv  Detail & Related papers  (2023-03-31T09:23:27Z)
• Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan [40.51754649947294]
  The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. The model's diagnostic performance was compared with clinicians's performance. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation.
  arXiv  Detail & Related papers  (2023-02-02T08:45:17Z)
• MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network [0.18374319565577152]
  Isocitrate dehydrogenase mutation and 1p/19q codeletion status are important prognostic markers for glioma. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. A 2.5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status.
  arXiv  Detail & Related papers  (2022-10-07T18:46:39Z)
• A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI [0.28675177318965034]
  Two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs) In the first phase, a novel deep boosted features and ensemble classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy individuals effectively. In the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types.
  arXiv  Detail & Related papers  (2022-01-14T10:24:47Z)
• Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data [0.32622301272834525]
  We predict the overall survival (OS) of glioma patients from diverse multimodal data with a Deep Orthogonal Fusion model. The model learns to combine information from MRI exams, biopsy-based modalities, and clinical variables into a comprehensive multimodal risk score. It significantly stratifies glioma patients by OS within clinical subsets, adding further granularity to prognostic clinical grading and molecular subtyping.
  arXiv  Detail & Related papers  (2021-07-01T17:59:01Z)
• Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas [65.64363834322333]
  Confidence Guided SAMR (CG-SAMR) synthesizes data from lesion information to multi-modal anatomic sequences. module guides the synthesis based on confidence measure about the intermediate results. experiments on real clinical data demonstrate that the proposed model can perform better than the state-of-theart synthesis methods.
  arXiv  Detail & Related papers  (2020-08-06T20:20:22Z)

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.