Proposing method to Increase the detection accuracy of stomach cancer based on colour and lint features of tongue using CNN and SVM

• URL: http://arxiv.org/abs/2011.09962v1
• Date: Wed, 18 Nov 2020 12:06:29 GMT
• Title: Proposing method to Increase the detection accuracy of stomach cancer based on colour and lint features of tongue using CNN and SVM
• Authors: Elham Gholami, Seyed Reza Kamel Tabbakh, Maryam Kheirabadi
• Abstract summary: The region of tongue is first separated from the face image by deep RCNN colorblack Recursive Convolutional Neural Network (R-CNN) colorblack. The results show that the proposed method is correctly able to identify the area of the tongue as well as the patient's person from the non-patient.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Today, gastric cancer is one of the diseases which affected many people's life. Early detection and accuracy are the main and crucial challenges in finding this kind of cancer. In this paper, a method to increase the accuracy of the diagnosis of detecting cancer using lint and colour features of tongue based on deep convolutional neural networks and support vector machine is proposed. In the proposed method, the region of tongue is first separated from the face image by {deep RCNN} \color{black} Recursive Convolutional Neural Network (R-CNN) \color{black}. After the necessary preprocessing, the images to the convolutional neural network are provided and the training and test operations are triggered. The results show that the proposed method is correctly able to identify the area of the tongue as well as the patient's person from the non-patient. Based on experiments, the DenseNet network has the highest accuracy compared to other deep architectures. The experimental results show that the accuracy of this network for gastric cancer detection reaches 91% which shows the superiority of method in comparison to the state-of-the-art methods.

Related papers

• ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imaging [0.0]
  We introduce an innovative convolutional ensemble network approach named deep autoencoder (DAE) with ResNet101. This method utilizes convolution-based deep neural networks for the detection of skin cancer. The methods result in 96.03% of accuracy, 95.40 % of precision, 96.05% of recall, 0.9576 of F-measure, 0.98 of AUC.
  arXiv  Detail & Related papers  (2024-03-21T09:07:28Z)
• 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)
• Convolutional neural network based on transfer learning for breast cancer screening [0.0]
  In this paper, a deep convolutional neural network-based algorithm is proposed to aid in accurately identifying breast cancer from ultrasonic images. Several experiments were conducted on the breast ultrasound dataset consisting of 537 Benign, 360 malignant, and 133 normal images. Using k-fold cross-validation and a bagging ensemble, we achieved an accuracy of 99.5% and a sensitivity of 99.6%.
  arXiv  Detail & Related papers  (2021-12-22T02:27:12Z)
• CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images [58.720142291102135]
  We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN) For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
  arXiv  Detail & Related papers  (2021-11-16T15:03:42Z)
• BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading [0.0]
  Deep learning has been recently adopted widely in different areas of science, especially medicine. In breast cancer detection problems, some diverse deep learning techniques have been developed on different datasets and resulted in good accuracy.
  arXiv  Detail & Related papers  (2021-07-11T12:55:33Z)
• 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)
• Deep learning for COVID-19 diagnosis based feature selection using binary differential evolution algorithm [1.332091725929965]
  The new Coronavirus is spreading rapidly and has taken the lives of many people so far. Deep Convolution neural networks are a powerful tool in classifying images. Our results demonstrate the suggested approach is better than recent studies on COVID-19 detection with X-ray images.
  arXiv  Detail & Related papers  (2021-04-15T07:12:58Z)
• Colorectal Cancer Segmentation using Atrous Convolution and Residual Enhanced UNet [0.5353034688884528]
  We propose a CNN-based approach, which uses atrous convolutions and residual connections besides the conventional filters. The proposed AtResUNet was trained on the DigestPath 2019 Challenge dataset for colorectal cancer segmentation with results having a Dice Coefficient of 0.748.
  arXiv  Detail & Related papers  (2021-03-16T19:20:20Z)
• 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)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)

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.