Breast Cancer Detection Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2003.07911v3
• Date: Wed, 19 Aug 2020 06:11:54 GMT
• Title: Breast Cancer Detection Using Convolutional Neural Networks
• Authors: Simon Hadush, Yaecob Girmay, Abiot Sinamo, Gebrekirstos Hagos
• Abstract summary: Breast cancer is prevalent in Ethiopia that accounts 34% among women cancer patients. Deep learning techniques are revolutionizing the field of medical image analysis. Our model detects mass region and classifies them into benign or malignant abnormality in mammogram(MG) images at once.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Breast cancer is prevalent in Ethiopia that accounts 34% among women cancer patients. The diagnosis technique in Ethiopia is manual which was proven to be tedious, subjective, and challenging. Deep learning techniques are revolutionizing the field of medical image analysis and hence in this study, we proposed Convolutional Neural Networks (CNNs) for breast mass detection so as to minimize the overheads of manual analysis. CNN architecture is designed for the feature extraction stage and adapted both the Region Proposal Network (RPN) and Region of Interest (ROI) portion of the faster R-CNN for the automated breast mass abnormality detection. Our model detects mass region and classifies them into benign or malignant abnormality in mammogram(MG) images at once. For the proposed model, MG images were collected from different hospitals, locally.The images were passed through different preprocessing stages such as gaussian filter, median filter, bilateral filters and extracted the region of the breast from the background of the MG image. The performance of the model on test dataset is found to be: detection accuracy 91.86%, sensitivity of 94.67% and AUC-ROC of 92.2%.

Related papers

• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Breast cancer detection using deep learning [0.0]
  This paper proposes a deep learning model for breast cancer detection from reconstructed images of microwave imaging scan data. NASNetLarge is the best architecture which can be used for the CNN model with accuracy of 88.41% and loss of 27.82%.
  arXiv  Detail & Related papers  (2023-04-20T15:22:12Z)
• A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images [0.0]
  A Deep Neural Network (DNN) is commonly employed to improve accuracy and breast cancer detection. We have analyzed pre-trained deep transfer learning models for detecting breast cancer using the 2453 histopathology images dataset. After analyzing the transfer learning model, we found that ResNet50 outperformed other models, achieving accuracy rates of 90.2%, Area under Curve (AUC) rates of 90.0%, recall rates of 94.7%, and a marginal loss of 3.5%.
  arXiv  Detail & Related papers  (2023-04-11T07:17:55Z)
• 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)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• 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)
• Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images [84.03487786163781]
  We develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection modules. Our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation.
  arXiv  Detail & Related papers  (2021-04-05T13:15:22Z)
• DenseNet for Breast Tumor Classification in Mammographic Images [0.0]
  The aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to features selection and extraction; and the classification was carried out by DenseNet architecture.
  arXiv  Detail & Related papers  (2021-01-24T03:30:59Z)
• Comparison of different CNNs for breast tumor classification from ultrasound images [12.98780709853981]
  classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. We compared different Convolutional Neural Networks (CNNs) and transfer learning methods for the task of automated breast tumor classification. The best performance was obtained by fine tuning VGG-16, with an accuracy of 0.919 and an AUC of 0.934.
  arXiv  Detail & Related papers  (2020-12-28T22:54:08Z)
• Segmentation of Infrared Breast Images Using MultiResUnet Neural Network [0.0]
  We are investigating infrared (IR) thermography as a noninvasive adjunct to mammography for breast cancer screening. Autoencoder-like convolutional and deconvolutional neural networks (C-DCNN) had been applied to automatically segment the breast area in IR images in previous studies. In this study, we applied a state-of-the-art deep-learning segmentation model, MultiResUnet, which consists of an encoder part to capture features and a decoder part for precise localization.
  arXiv  Detail & Related papers  (2020-10-31T22:15:28Z)
• Understanding the robustness of deep neural network classifiers for breast cancer screening [52.50078591615855]
  Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. We measure the sensitivity of a radiologist-level screening mammogram image classifier to four commonly studied input perturbations. We also perform a detailed analysis on the effects of low-pass filtering, and find that it degrades the visibility of clinically meaningful features.
  arXiv  Detail & Related papers  (2020-03-23T01:26:36Z)

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.