Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning

Version 1 : Received: 28 February 2023 / Approved: 1 March 2023 / Online: 1 March 2023 (08:51:04 CET)

A peer-reviewed article of this Preprint also exists.

Hanis, T.M.; Ruhaiyem, N.I.R.; Arifin, W.N.; Haron, J.; Wan Abdul Rahman, W.F.; Abdullah, R.; Musa, K.I. Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning. Diagnostics 2023, 13, 1780. Hanis, T.M.; Ruhaiyem, N.I.R.; Arifin, W.N.; Haron, J.; Wan Abdul Rahman, W.F.; Abdullah, R.; Musa, K.I. Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning. Diagnostics 2023, 13, 1780.

Abstract

This study utilised an ensemble of pre-trained networks and digital mammograms to develop a supplementary diagnostic tool for radiologists. Digital mammograms and their associated information were collected from the department of radiology and pathology, Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and explored in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks based on PR-AUC, precision, and F1 score. The final ensemble model had a mean precision, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated a balanced performance across mammographic density. In conclusion, this study exhibited the good performance of the ensemble transfer learning on digital mammograms for the purpose of breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus, reducing their workloads.

Keywords

Asian women; breast cancer; transfer learning; artificial intelligence

Subject

Medicine and Pharmacology, Oncology and Oncogenics

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