This research introduces a sophisticated transfer learning model based on
Google's MobileNetV2 for breast cancer tumor classification into normal,
benign, and malignant categories, utilizing a dataset of 1576 ultrasound images
(265 normal, 891 benign, 420 malignant). The model achieves an accuracy of
0.82, precision of 0.83, recall of 0.81, ROC-AUC of 0.94, PR-AUC of 0.88, and
MCC of 0.74. It examines image intensity distributions and misclassification
errors, offering improvements for future applications. Addressing dataset
imbalances, the study ensures a generalizable model. This work, using a dataset
from Baheya Hospital, Cairo, Egypt, compiled by Walid Al-Dhabyani et al.,
emphasizes MobileNetV2's potential in medical imaging, aiming to improve
diagnostic precision in oncology. Additionally, the paper explores
Streamlit-based deployment for real-time tumor classification, demonstrating
MobileNetV2's applicability in medical imaging and setting a benchmark for
future research in oncology diagnostics.

基于 Google 的 MobileNetV2 模型，利用来自埃及开罗的 Baheya 医院的数据集，该研究引入了一种复杂的转移学习模型，用于对乳腺癌肿瘤进行分类，分为正常、良性和恶性三类，采用 1576 张超声图像（265 张正常、891 张良性、420 张恶性）。该模型实现了 0.82 的准确率，0.83 的精确率，0.81 的召回率，0.94 的 ROC-AUC，0.88 的 PR-AUC 和 0.74 的 MCC。它研究了图像强度分布和错误分类，为未来的应用提供了改进。通过解决数据集不平衡问题，该研究确保了一个具有普适性的模型。此外，本论文探讨了基于 Streamlit 的实时肿瘤分类部署，展示了 MobileNetV2 在医学成像中的适用性，并为肿瘤诊断领域的未来研究设定了一个基准。

利用 MobileNetV2 进行增强的乳腺癌瘤分类：对图像强度、错误修正和基于 Streamlit 的实时部署的详细探索

Enhanced Breast Cancer Tumor Classification using MobileNetV2: A  Detailed Exploration on Image Intensity, Error Mitigation, and  Streamlit-driven Real-time Deployment

Digitized Histological diagnosis is in increasing demand. However, color
variations due to various factors are imposing obstacles to the diagnosis
process. The problem of stain color variations is a well-defined problem with
many proposed solutions. Most of these solutions are highly dependent on a
reference template slide. We propose a deep-learning solution inspired by
CycleGANs that is trained end-to-end, eliminating the need for an expert to
pick a representative reference slide. Our approach showed superior results
quantitatively and qualitatively against the state of the art methods (10%
improvement visually using SSIM). We further validated our method on a clinical
use-case, namely Breast Cancer tumor classification, showing 12% increase in
AUC. The code will be made publicly available.

利用深度学习方法的 CycleGANs，不需要专家选择代表性的参考标本，解决了数字化组织学诊断中由于多种因素引起的染色剂颜色变化引起的难题，并在乳腺癌肿瘤分类等临床应用中得到了验证。