Advanced Breast Cancer Diagnostics With PolyBreastVit: A Combined PolyNet and Vision Transformer Approach - Visalakshi Annepu; Mohamed Abbas; Hanumatha Rao Bitra; Naveen Kumar Vaegae; Kalapraveen Bagadi (Applied Computational Intelligence and Soft Computing)
Breast cancer continues to be an important health issue around the world, with timely screening being important in improving survival and therapy. Here is a presentation of PolyBreastVit, a novel hybrid deep learning (DL) model for the automatic detection and classification of breast cancer in ultrasound images that combines PolyNet with Vision Transformer (ViT). The above model is trained and validated on a dataset of 880 high-definition images collected from 500 female subjects aged between 25 and 75 years on three classes: benign, malignant, and normal. For the enhancement of the proposed model’s accuracy, thorough data augmentation and preprocessing have been performed. The performance of PolyBreastVit is evaluated against several well-known DL models such as VGG-16, Inception V3, and ResNet-50 using accuracy, precision, recall, F1, AUC, and other standard metrics. These findings support the evidence that PolyBreastVit manages to outperform those classical models in the task of breast cancer classification in every aspect. This paper presents the latest development of breast cancer diagnostic tools through medical imaging incorporating convolutional neural networks (CNNs) and transformer models for radiologists.