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

TL;DR

This paper leverages MobileNetV2 for accurate breast cancer tumor classification from ultrasound images, analyzing image intensities, error sources, and deploying a real-time app, advancing medical imaging diagnostics.

Contribution

It introduces a MobileNetV2-based transfer learning approach with real-time deployment for breast cancer classification, addressing dataset imbalance and error analysis.

Findings

Accuracy of 0.82 achieved

Effective real-time deployment with Streamlit

Insights into image intensity and error sources

Abstract

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.