用深度学习模型自动识别乳腺超声图像，准确率超98%。
Early Detection and Classification of Breast Cancer Using Deep Learning Techniques
- 采用ResNet50等4个预训练模型加自定义分类器
- 最高准确率达98.41%，优于其他模型
- 适合医学影像分析与早期癌症筛查研究者
乳腺癌是全球每年导致大量死亡的致命癌症之一。当乳腺组织异常快速增生时，即发展为乳腺癌。若能在恶性前发现，可有效降低致死率。本研究利用人工智能与机器学习技术实现早期自动化检测。基于Kaggle数据集的9248张乳腺超声图像，分为良性、恶性和正常三类。实验引入ResNet50、MobileNet、VGG16三个预训练模型及一个自定义CNN模型，均使用ReLU激活函数。结果表明，ResNet50、MobileNet、VGG16和自定义CNN的准确率分别为98.41%、97.91%、98.19%和92.94%，其中ResNet50表现最优。该模型凭借深层结构在识别异常细胞及良恶性肿瘤方面尤为出色。结果显示，机器学习方法在乳腺癌分类与早期检测中具有高度适用性。
原文摘要 · Abstract (English)
Breast cancer is one of the deadliest cancers causing about massive number of patients to die annually all over the world according to the WHO. It is a kind of cancer that develops when the tissues of the breast grow rapidly and unboundly. This fatality rate can be prevented if the cancer is detected before it gets malignant. Using automation for early-age detection of breast cancer, Artificial Intelligence and Machine Learning technologies can be implemented for the best outcome. In this study, we are using the Breast Cancer Image Classification dataset collected from the Kaggle depository, which comprises 9248 Breast Ultrasound Images and is classified into three categories: Benign, Malignant, and Normal which refers to non-cancerous, cancerous, and normal images.This research introduces three pretrained model featuring custom classifiers that includes ResNet50, MobileNet, and VGG16, along with a custom CNN model utilizing the ReLU activation function.The models ResNet50, MobileNet, VGG16, and a custom CNN recorded accuracies of 98.41%, 97.91%, 98.19%, and 92.94% on the dataset, correspondingly, with ResNet50 achieving the highest accuracy of 98.41%.This model, with its deep and powerful architecture, is particularly successful in detecting aberrant cells as well as cancerous or non-cancerous tumors. These accuracies show that the Machine Learning methods are more compatible for the classification and early detection of breast cancer.
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