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

arXiv: https://arxiv.org/abs/2109.07878 · HTML: https://mlsanity.com/paper/2109.07878/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Maxout Networks](https://mlsanity.com/paper/1302.4389/) (2013)
- [Activate or Not: Learning Customized Activation](https://mlsanity.com/paper/2009.04759/) (2020)
- [Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data](https://mlsanity.com/paper/1509.04619/) (2015)
- [Automatic Discrimination of Color Retinal Images using the Bag of Words Approach](https://mlsanity.com/paper/1603.04327/) (2016)

## Built on by (most cited first, 1 of 1)

- [Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT](https://mlsanity.com/paper/2304.04920/) (2023)

## Abstract (arXiv)

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 image to EfficientNetV2-SA network, and then the classifier trained by transfer learning will return the diagnosis results. We verify the performance of our chatbot and classification on the extrinsic metrics and BreaKHis dataset, respectively. The task completion rate of M-Chatbot reached 63.33%. For the BreaKHis dataset, the highest accuracy of EfficientNetV2-SA network have achieved 84.71%. All these experimental results illustrate that the proposed model can improve the accuracy performance of image recognition and our new intelligent medical diagnosis system is successful and efficient in providing automatic diagnosis of breast cancer.