Breast Cancer Diagnosis in Thermography Using Pre-Trained VGG16 with Deep Attention Mechanisms

Abstract

One of the most prevalent cancers in women is breast cancer. The mortality rate related to this disease can be decreased by early, accurate diagnosis to increase the chance of survival. Infrared thermal imaging is one of the breast imaging modalities in which the temperature of the breast tissue is measured using a screening tool. The previous studies did not use pre-trained deep learning (DL) with deep attention mechanisms (AMs) on thermographic images for breast cancer diagnosis. Using thermal images from the Database for Research Mastology with Infrared Image (DMR-IR), the study investigates the use of a pre-trained Visual Geometry Group with 16 layers (VGG16) with AMs that can produce good diagnosis performance utilizing the thermal images of breast cancer. The symmetry of the three models resulting from the combination of VGG16 with three types of AMs is evident in all its stages in methodology. The models were compared to state-of-art breast cancer diagnosis approaches and tested for accuracy, sensitivity, specificity, precision, F1-score, AUC score, and Cohen’s kappa. The test accuracy rates for the AMs using the VGG16 model on the breast thermal dataset were encouraging, at 99.80%, 99.49%, and 99.32%. Test accuracy for VGG16 without AMs was 99.18%, whereas test accuracy for VGG16 with AMs improved by 0.62%. The proposed approaches also performed better than previous approaches examined in the related studies.

A Review of Artificial Intelligence in Breast Imaging

“…Thermal images from the Database for Mastology Research with Infrared Images (DMR-IR) were employed in [89] to explore the effectiveness of VGG16, a pre-trained CNN architecture, in combination with attention mechanisms (AMs) for diagnosing breast cancer. The investigation focused on three variants of the model, each incorporating a distinct type of AM.…”

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024

“…In 2022, Alshehri et al used attention mechanisms (AMs) to improve the detection performance of a CNN-based thermogram classifier, achieving up to 99.46% accuracy versus 92.3% for a CNN without AM [110]. In a subsequent early 2023 study, the same authors achieved up to 99.8% accuracy by using a deeper CNN architecture coupled with AMs [111].…”