  Objective: This paper proposes a deep learning model for breast cancer
detection from reconstructed images of microwave imaging scan data and aims to
improve the accuracy and efficiency of breast tumor detection, which could have
a significant impact on breast cancer diagnosis and treatment. Methods: Our
framework consists of different convolutional neural network (CNN)
architectures for feature extraction and a region-based CNN for tumor
detection. We use 7 different architectures: DenseNet201, ResNet50,
InceptionV3, InceptionResNetV3, MobileNetV2, NASNetMobile and NASNetLarge and
compare its performance to find the best architecture out of the seven. An
experimental dataset of MRI-derived breast phantoms was used. Results:
NASNetLarge is the best architecture which can be used for the CNN model with
accuracy of 88.41% and loss of 27.82%. Given that the model's AUC is 0.786, it
can be concluded that it is suitable for use in its present form, while it
could be improved upon and trained on other datasets that are comparable.
Impact: One of the main causes of death in women is breast cancer, and early
identification is essential for enhancing the results for patients. Due to its
non-invasiveness and capacity to produce high-resolution images, microwave
imaging is a potential tool for breast cancer screening. The complexity of
tumors makes it difficult to adequately detect them in microwave images. The
results of this research show that deep learning has a lot of potential for
breast cancer detection in microwave images
