Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning

• URL: http://arxiv.org/abs/2003.13503v3
• Date: Wed, 27 May 2020 05:53:17 GMT
• Title: Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
• Authors: Aditya Khamparia, Subrato Bharati, Prajoy Podder, Deepak Gupta, Ashish Khanna, Thai Kim Phung, Dang N. H. Thanh
• Abstract summary: This paper focuses on transfer learning process to detect breast cancer. Modified VGG 16, residual network, mobile network is proposed and implemented in this paper.
• Score: 5.835732870341059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper. DDSM dataset is used in this paper. Experimental results show that our proposed hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an accuracy of 88.3% where the number of epoch is 15. On the other hand, only modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet provides an accuracy of 77.2%. So, it is clearly stated that the proposed hybrid pre-trained network outperforms well compared to single architecture. This architecture can be considered as an effective tool for the radiologists in order to reduce the false negative and false positive rate. Therefore, the efficiency of mammography analysis will be improved.

Related papers

• Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging [65.83291923029985]
  Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment PCa lesions from a 200 CDI$
  arXiv  Detail & Related papers  (2025-01-15T22:23:41Z)
• Analysis of Transferred Pre-Trained Deep Convolution Neural Networks in Breast Masses Recognition [3.3686252536891454]
  The effect of layer freezing in a pre-trained CNN is investigated for breast cancer detection by classifying mammogram images as benign or malignant. The best recognition rate was obtained from a frozen first block of VGG19 with a sensitivity of 95.64 %, while the training of the entire VGG19 yielded 94.48%.
  arXiv  Detail & Related papers  (2024-12-23T20:16:45Z)
• Deep Transfer Learning for Breast Cancer Classification [0.0]
  Deep transfer learning has emerged as a promising technique for improving breast cancer classification. In this study, we examine the use of a VGG, Vision Transformers (ViT) and Resnet to classify images for Invasive Ductal Carcinoma (IDC) cancer. The result shows a great advantage of Resnet-34 with an accuracy of $90.40%$ in classifying cancer images.
  arXiv  Detail & Related papers  (2024-09-05T15:54:41Z)
• 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)
• CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images [0.0]
  Breast cancer remains the major one for being the reason of largest number of demise of women. In the recent time of research, Medical Image Computing and Processing has been playing a significant role for detecting and classifying breast cancers from ultrasound images and mammograms, along with deep neural networks. In this research, we focused mostly on our rigorous implementations and iterative result analysis of different cutting-edge modified versions of EfficientNet.
  arXiv  Detail & Related papers  (2023-08-25T13:05:06Z)
• 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)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• Multi-Head Feature Pyramid Networks for Breast Mass Detection [48.24995569980701]
  We propose the multi-head feature pyramid module (MHFPN) to solve the problem of unbalanced focus of target boxes during feature map fusion. Experimental studies show that, comparing to the SOTA detection baselines, our method improves by 6.58% (in AP@50) and 5.4% (in TPR@50) on the commonly used INbreast dataset.
  arXiv  Detail & Related papers  (2023-02-22T03:02:52Z)
• Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods [0.0]
  Deep feature transfer learning is used as the main idea of the proposed CAD system's feature extractor. Inception-ResNet-v2 has shown the best feature extraction capability in the case of breast cancer histopathology images. In the classification phase, the ensemble of CatBoost, XGBoost and LightGBM has provided the best average accuracy.
  arXiv  Detail & Related papers  (2022-09-03T09:27:00Z)
• 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)
• 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)

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.