Butler Journal of Undergraduate Research
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 and colleagues (2020), 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. Keywords: MobileNetV2, image intensity error mitigation, Streamlit deployment, transfer learning, deep learning in oncology, ultrasound imaging, classification accuracy, convolutional neural networks (CNNs), medical image processing
Recommended Citation
Surya, Aaditya; Shah, Adita Keshary; Sasikumar, Subash Tarun; and Kabore, Jarnell
(2024)
"Enhanced Breast Cancer Tumor Classification Using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-Driven Real-Time Deployment,"
Butler Journal of Undergraduate Research: Vol. 10
, Article 8.
Retrieved from:
https://digitalcommons.butler.edu/bjur/vol10/iss1/8