Breast Cancer Detection with VGG16: A Deep Learning Approach with Thermographic Imaging
Keywords:
BC detection, Convolutional neural network, Deep learning, Data augmentation, Machine learning, Normalization, Thermal imaging, VGG16Abstract
Breast Cancer (BC) still stands as a major global health issue that demands state of the art diagnostic instruments for early detection and mortality reduction. It is a radiation-free, non-invasive method of detecting temperatures and might be used as an adjuvant next to the regular methods. This study establishes a deep learning (DL) model with the state-of-the-art method using transfer learning and pre-trained VGG16 convolutional neural network. We train and evaluate the model on thermal images taken from Database for Research in Mastology with Infrared Images (DMR-IR). They also use augmentation and normalization methods to improve model performance. The DL-based model obtained a promising detection rate of 99.4% for the prediction of BC lesions. It demonstrates a sensitivity of 100%, specificity of 97.5%, Recall of 99%, Precision of 98.9%, F1-Score of 99.8%, and AUC-ROC of 99.8%, surpassing previous models. Comparative analysis with alternative methods such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Decision trees (DT), and Gradient Boosting (GB) underscores the superior performance of the DL approach. This research presents a groundbreaking approach to BC detection, leveraging deep learning and thermal imaging technology. The findings highlight the efficacy of the proposed DL model in enhancing diagnostic accuracy and supporting informed decision-making in clinical settings. Future research could explore broader applications and integration into healthcare practices to improve patient outcomes.
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