Multi-Attention Integrated Deep Learning Frameworks for Enhanced Breast Cancer Segmentation and Identification
<|reference_start|>Multi-Attention Integrated Deep Learning Frameworks for Enhanced Breast Cancer Segmentation and Identification: Breast cancer poses a profound threat to lives globally, claiming numerous lives each year. Therefore, timely detection is crucial for early intervention and improved chances of survival. Accurately diagnosing and classifying breast tumors using ultrasound images is a persistent challenge in medicine, demanding cutting-edge solutions for improved treatment strategies. This research introduces multiattention-enhanced deep learning (DL) frameworks designed for the classification and segmentation of breast cancer tumors from ultrasound images. A spatial channel attention mechanism is proposed for segmenting tumors from ultrasound images, utilizing a novel LinkNet DL framework with an InceptionResNet backbone. Following this, the paper proposes a deep convolutional neural network with an integrated multi-attention framework (DCNNIMAF) to classify the segmented tumor as benign, malignant, or normal. From experimental results, it is observed that the segmentation model has recorded an accuracy of 98.1%, with a minimal loss of 0.6%. It has also achieved high Intersection over Union (IoU) and Dice Coefficient scores of 96.9% and 97.2%, respectively. Similarly, the classification model has attained an accuracy of 99.2%, with a low loss of 0.31%. Furthermore, the classification framework has achieved outstanding F1-Score, precision, and recall values of 99.1%, 99.3%, and 99.1%, respectively. By offering a robust framework for early detection and accurate classification of breast cancer, this proposed work significantly advances the field of medical image analysis, potentially improving diagnostic precision and patient outcomes.<|reference_end|>
@article{v2024exploiting,
    title={Exploiting Precision Mapping and Component-Specific Feature Enhancement
  for Breast Cancer Segmentation and Identification},
    author={Pandiyaraju V, Shravan Venkatraman, Pavan Kumar S, Santhosh
  Malarvannan, Kannan A},
    journal={arXiv preprint arXiv:2407.02844},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02844},
    primaryClass={eess.IV cs.CV cs.LG}
}