A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer

  title={A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer},
  author={Shiyu Fan and Runhai Xu and Zhaohang Yan},
  journal={2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)},
  • Shiyu Fan, Runhai Xu, Zhaohang Yan
  • Published 16 September 2021
  • Computer Science
  • 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
This paper constructs a novel intelligent medical diagnosis system, which can realize automatic communication and breast cancer pathological image recognition. This system contains two main parts, including a pre-training chatbot called M-Chatbot and an improved neural network model of EfficientNetV2-S named EfficientNetV2-SA, in which the activation function in top layers is replaced by ACON-C. Using information retrieval mechanism, M-Chatbot instructs patients to send breast pathological… 

Figures and Tables from this paper


A Dataset for Breast Cancer Histopathological Image Classification
A dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.ufpr.br/vri/breast-cancer-database, aimed at automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician.
Medical Image Classification via SVM Using LBP Features from Saliency-Based Folded Data
This paper proposes to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions, and demonstrates the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.
Medical image analysis with artificial neural networks
A focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing is provided to increase awareness of how neural networks can be applied to these areas.
Medical image classification with convolutional neural network
A customized Convolutional Neural Networks with shallow convolution layer to classify lung image patches with interstitial lung disease and the same architecture can be generalized to perform other medical image or texture classification tasks.
ImageNet classification with deep convolutional neural networks
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Automatic discrimination of color retinal images using the bag of words approach
The role of bag of words approach in the automatic diagnosis of retinopathy diabetes is investigated and single-based and multiple-based methods to construct the visual dictionary are proposed by combining the histogram of word occurrences from each dictionary and building a single histogram.
Deep Residual Learning for Image Recognition
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
A novel approach for medical assistance using trained chatbot
The proposed idea is to create a system with artificial intelligence that can predict the diseases based on the symptoms and give the list of available treatments and the composition of the medicines and their prescribed uses.
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.