Proposed Deep Learning Classification and Ultra-Wide Band Tensors for Localization of Breast Tumor

Due to the increase in high-performance computing facilities, deep learning techniques that implement deep neural networks have become common. Also, the ability to process a large range of features as it deals with unstructured data, deep learning achieves greater strength and versatility. Initial layers extract low-level features, and subsequent layers combine features to create a full representation. An overview of the evolution of deep learning models and a brief idea about the different learning approaches, such as supervised learning, to train the neural network, supervised learning utilizes labelled data. Different architectures Pretrained Networks, Convolution Neural Networks, used to implement deep learning. Medical screening techniques have become increasingly important in the diagnosis and treatment of diseases. Breast cancer early detection is thought to be a key factor in lowering women's death rates. To boost breast cancer diagnosis, several alternative breast screening modalities are being studied. A modern methodology for cancer diagnosis and localization uses ultra-wide band (UWB) radar for screening breast cancer and identifying the presence of tumors in the breast using artificial intelligence techniques. This work focused on experimental data set based on breast tumor detection and localization using convolution neural network (CNN) techniques (i.e., pretrained CNN). CNNs are a useful way to find solutions to real-world challenges, and neural networks with learning algorithms are an exciting emerging technology.