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

TL;DR

This paper presents an intelligent medical diagnosis system combining a pre-training chatbot and an improved neural network for automatic breast cancer image recognition, achieving high accuracy and efficiency.

Contribution

It introduces a novel system integrating M-Chatbot and an enhanced EfficientNetV2-SA model for automatic breast cancer diagnosis from histopathological images.

Findings

Chatbot task completion rate of 63.33%

Achieved 84.71% accuracy on BreaKHis dataset

Improved image recognition accuracy with the proposed model

Abstract

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.