Advanced multimodal for Segmentation and Classification of breast cancer with DenseNet and Optimal Attention approach.

BACKGROUND AND

OBJECTIVE

The early detection of breast cancer plays a critical role in improving survival rates and facilitating precise medical interventions. Therefore, the automated identification of breast abnormalities becomes paramount, significantly enhancing the prospects of successful treatment outcomes. To address this imperative, our research leverages multiple modalities such as MRI, CT, and mammography to detect and screen for breast cancer.

METHODS

Our proposed methodology integrates innovative modules such as the Distributed Load-based Dilated Convolution (DLM) and Multi-Head Dense Dilated Convolution (MHD) modules. These modules are designed to capture multi-scale information crucial for accurate tumor detection while simultaneously minimizing the parameter count, thus enhancing computational efficiency. Additionally, an Optimal Multi-Level Attention (OMLA) and Key Aspect Attention (KA) modules are adept at aggregating task-relevant information effectively, contributing to improved model performance and accuracy.

RESULTS

Our experimental evaluation utilizing multimodal clinical data underscores the effectiveness of the proposed method, achieving an impressive Dice coefficient of 86.756%, 96.931%, and 97.813% for segmentation and classification accuracy of 89.982%, 97.028%, and 98.760% for Mammogram, CT, and MRI datasets, respectively. These results validate the efficacy and potential clinical utility of our approach in enhancing breast cancer detection and diagnosis.

CONCLUSIONS

We introduce a novel framework for the classification and segmentation of multimodal medical images acquired by a variety of modalities for breast cancer detection and identification. In addition to improving segmentation accuracy, our proposed Computer-Aided Diagnosis (CAD) method utilizes the segmentation mask as an additional channel feature to enhance the classification outcomes of breast tumors. Additionally, we have meticulously curated a comprehensive CT breast cancer dataset, comprising various clinical data types related to breast tumors, including detailed segmentation and classification annotations. This dataset surpasses previous efforts in its depth and breadth, providing a robust foundation for future research endeavors.