Multi-Input Deep Learning Approach for Breast Cancer Screening Using Thermal Infrared Imaging and Clinical Data

Abstract

Breast cancer is one of the most prevalent causes of death among women across the globe. Early detection is the best strategy for reducing the mortality rate. Currently, mammography is the standard screening modality, which has its shortcomings. To complement this modality, thermal infrared-based Computer-Aided Diagnosis (CADx) tools have been presented as economical, less hazardous, and a suitable solution for various age groups. Although a viable solution, most CADx systems are built primarily from frontal breast thermograms, and are likely to miss lesions that may develop on the sides. Additionally, these systems often disregard critical clinical data, such as risk factors. This paper presents a novel CADx system that utilizes deep learning techniques for breast cancer detection. The system incorporates multiple breast thermogram views and corresponding patient clinical data to improve the accuracy of the diagnosis. We describe the methodology of the system, including the extraction of regions of interest from images and the use of transfer learning to train three different models. We evaluate the performance of the models and compare them to similar works from the literature. The results demonstrate that using multi-inputs outperforms single-input models and achieves an overall accuracy of 90.48%, a sensitivity of 93.33%, and an AUROC curve of 0.94. This approach could offer a more cost-effective and less hazardous screening option for breast cancer detection, particularly for a wide range of age groups.

Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach

“…A data point's class is determined by taking the majority class among its k closest neighbors, which are found using these distance measures [19]- [21].…”

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging

“…Also, to efficiently handle large data and deliver timely predictions, a network connection with high bandwidth and low latency is mandatory. An alternative approach is to use each client's data to train the ML model and then distribute copies of the trained model to each participant [20][21][22][23]. This way, data doesn't need to be moved when new insights are gained, as each owner has their model.…”