Study for Performance of MobileNetV1 and MobileNetV2 Based on Breast Cancer

TL;DR

This study compares MobileNetV1 and MobileNetV2 models in detecting breast cancer from histopathological images, finding MobileNetV1 to be more accurate and less prone to overfitting on the Kaggle dataset.

Contribution

It provides a comparative analysis of MobileNetV1 and V2 for breast cancer image classification, highlighting MobileNetV1's superior performance in this context.

Findings

MobileNetV1 achieved higher validation accuracy.

MobileNetV1 showed less overfitting than MobileNetV2.

MobileNetV1 outperformed MobileNetV2 on the dataset.

Abstract

Artificial intelligence is constantly evolving and can provide effective help in all aspects of people's lives. The experiment is mainly to study the use of artificial intelligence in the field of medicine. The purpose of this experiment was to compare which of MobileNetV1 and MobileNetV2 models was better at detecting histopathological images of the breast downloaded at Kaggle. When the doctor looks at the pathological image, there may be errors that lead to errors in judgment, and the observation speed is slow. Rational use of artificial intelligence can effectively reduce the error of doctor diagnosis in breast cancer judgment and speed up doctor diagnosis. The dataset was downloaded from Kaggle and then normalized. The basic principle of the experiment is to let the neural network model learn the downloaded data set. Then find the pattern and be able to judge on your own whether breast tissue is cancer. In the dataset, benign tumor pictures and malignant tumor pictures have been classified, of which 198738 are benign tumor pictures and 78, 786 are malignant tumor pictures. After calling MobileNetV1 and MobileNetV2, the dataset is trained separately, the training accuracy and validation accuracy rate are obtained, and the image is drawn. It can be observed that MobileNetV1 has better validation accuracy and overfit during MobileNetV2 training. From the experimental results, it can be seen that in the case of processing this dataset, MobileNetV1 is much better than MobileNetV2.