C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification

• URL: http://arxiv.org/abs/2011.00081v2
• Date: Thu, 19 Aug 2021 02:44:51 GMT
• Title: C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification
• Authors: Hosein Barzekar, Zeyun Yu
• Abstract summary: We propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification.
• Score: 6.85316573653194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics.

Related papers

• Dual-channel Prototype Network for few-shot Classification of Pathological Images [0.7562219957261347]
  We introduce the Dual-channel Prototype Network (DCPN) to tackle the challenge of classifying pathological images with limited samples. DCPN augments the Pyramid Vision Transformer framework for few-shot classification via self-supervised learning and integrates it with convolutional neural networks. This combination forms a dual-channel architecture that extracts multi-scale, highly precise pathological features.
  arXiv  Detail & Related papers  (2023-11-14T03:03:21Z)
• Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification [0.0]
  We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements.
  arXiv  Detail & Related papers  (2023-07-24T13:39:21Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology [9.037868656840736]
  In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features. This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions. Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90%$.
  arXiv  Detail & Related papers  (2022-04-18T13:31:53Z)
• Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection [9.826027427965354]
  Recent advances have shown that the convolutional Neural Network (CNN) architectures can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. We consider some contemporary CNN models for binary classification of breast histopathology images. We have validated the trained CNN networks on a publicly available BreaKHis dataset, for 200x and 400x magnified histopathology images.
  arXiv  Detail & Related papers  (2022-01-04T03:09:40Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Automatic Semantic Segmentation of the Lumbar Spine. Clinical Applicability in a Multi-parametric and Multi-centre MRI study [0.0]
  This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different strategies.
  arXiv  Detail & Related papers  (2021-11-16T17:33:05Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)

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.