Multi-Model Learning for Cheast Cancer Detection: Combining Medical Images and Clinical Data using self –Attention with Dilated CNN and Transformers

Abstract

one of the leading causes of demise for women global continues to be breast most cancers, highlighting the importance of activate and particular detection. Self-Attention with Dilated Convolutional Neural Networks (CNNs) and transformer, a sort of deep mastering set of rules that has proven superb efficacy in clinical photograph and Clinical statistics analysis, offer a strong technique for predicting breast most cancers [2]. This observe investigates the significance of self-attention mechanisms and dilated convolution with CNN fashions for you to detect and classify breast cancer the usage of medical records and mammography pics. Given that the anomalous region in a mammography picture is much smaller than the normal region, CNN models consider all image regions equally. As a result, the anomalous region teaches the models less representational properties. This study uses dilated convolution in unification with a multi-level self-attention enhanced CNN model to classify cancer images. The capacity of the suggested. The dilated CNN component gathers multi-scale spatial information from medical images, while the Transformer layer guarantees long-range relationships between clinical features, providing a thorough picture of the patient's state. We show the effectiveness of this approach on a publicly available dataset comprising clinical and imaging data. The suggested attention-augmented CNN model outperforms conventional CNN techniques in terms of classification accuracy in distinguishing malignant and benign instances by automatically extracting more complicated characteristics from the cancerous zone [9]. In order to lower mortality rates and to enhance the results of breast cancer treatment for patients, the study shows how attention-augmented CNN-based systems might enhance early identification and treatment planning. Techniques like data augmentation, transfer learning, and explainable AI are used to overcome issues including class imbalance, data scarcity, and model interpretability. This work aims to help develop more personalized healthcare solutions by enabling the development of more reliable, data-driven diagnostic tools that could make use of both visible and invisible affected person statistics.