An Innovative Framework for Breast Cancer Detection Using Pyramid Adaptive Atrous Convolution, Transformer Integration, and Multi-Scale Feature Fusion

• URL: http://arxiv.org/abs/2601.12249v1
• Date: Sun, 18 Jan 2026 03:55:33 GMT
• Title: An Innovative Framework for Breast Cancer Detection Using Pyramid Adaptive Atrous Convolution, Transformer Integration, and Multi-Scale Feature Fusion
• Authors: Ehsan Sadeghi Pour, Mahdi Esmaeili, Morteza Romoozi,
• Abstract summary: This thesis presents an innovative framework for detecting malignant masses in mammographic images by integrating the Pyramid Adaptive Atrous Convolution (PAAC) and Transformer architectures.<n>The proposed approach utilizes Multi-Scale Feature Fusion to enhance the extraction of features from benign and malignant tissues and combines Dice Loss and Focal Loss functions to improve the model's learning process.<n>The proposed model achieved high accuracy in detecting cancerous masses, outperforming foundational models such as BreastNet, DeepMammo, Multi-Scale CNN, Swin-Unet, and SegFormer.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Breast cancer is one of the most common cancers among women worldwide, and its accurate and timely diagnosis plays a critical role in improving treatment outcomes. This thesis presents an innovative framework for detecting malignant masses in mammographic images by integrating the Pyramid Adaptive Atrous Convolution (PAAC) and Transformer architectures. The proposed approach utilizes Multi-Scale Feature Fusion to enhance the extraction of features from benign and malignant tissues and combines Dice Loss and Focal Loss functions to improve the model's learning process, effectively reducing errors in binary breast cancer classification and achieving high accuracy and efficiency. In this study, a comprehensive dataset of breast cancer images from INbreast, MIAS, and DDSM was preprocessed through data augmentation and contrast enhancement and resized to 227x227 pixels for model training. Leveraging the Transformer's ability to manage long-range dependencies with Self-Attention mechanisms, the proposed model achieved high accuracy in detecting cancerous masses, outperforming foundational models such as BreastNet, DeepMammo, Multi-Scale CNN, Swin-Unet, and SegFormer. The final evaluation results for the proposed model include an accuracy of 98.5\%, sensitivity of 97.8\%, specificity of 96.3\%, F1-score of 98.2\%, and overall precision of 97.9\%. These metrics demonstrate a significant improvement over traditional methods and confirm the model's effectiveness in identifying cancerous masses in complex scenarios and large datasets. This model shows potential as a reliable and efficient tool for breast cancer diagnosis and can be effectively integrated into medical diagnostic systems.

Related papers

• PSO-XAI: A PSO-Enhanced Explainable AI Framework for Reliable Breast Cancer Detection [2.5631347250059577]
  This study proposes an integrated framework that incorporates customized Particle Swarm Optimization (PSO) for feature selection.<n>The proposed approach achieved a superior score of 99.1% across all performance metrics, including accuracy and precision.<n>Results highlight the potential of combining swarm intelligence with explainable ML for robust, trustworthy, and clinically meaningful breast cancer diagnosis.
  arXiv  Detail & Related papers  (2025-10-23T14:42:50Z)
• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis [1.5939351525664014]
  We propose a transformer-based deep learning framework for multi-class tumor classification.<n>Our method addresses key limitations of conventional convolutional neural networks.<n>Our approach classification achieves accuracies of 99.32%, 96.92%, 95.28%, and 96.94% for breast, prostate, bone, and cervical cancers respectively.
  arXiv  Detail & Related papers  (2025-08-15T03:10:52Z)
• Breast Tumor Classification Using EfficientNet Deep Learning Model [5.062500255359342]
  We introduce an intensive data augmentation pipeline and cost-sensitive learning, improving representation and ensuring that the model does not overly favor majority classes.<n>Our results underscore significant improvements in the binary classification performance, achieving an exceptional recall increase for benign cases.
  arXiv  Detail & Related papers  (2024-11-26T20:38:33Z)
• Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning [0.0]
  This study introduces a novel approach leveraging EfficientNetV2 models to improve feature extraction and focus on relevant tissue regions.<n>The EfficientNetV2-XL with CBAM achieved outstanding performance, reaching a peak accuracy of 99.01 percent and an F1-score of 98.31 percent at 400X magnification.
  arXiv  Detail & Related papers  (2024-10-29T17:56:05Z)
• Exploiting Precision Mapping and Component-Specific Feature Enhancement for Breast Cancer Segmentation and Identification [0.0]
  We propose novel Deep Learning (DL) frameworks for breast lesion segmentation and classification.<n>We introduce a precision mapping mechanism (PMM) for a precision mapping and attention-driven LinkNet (PMAD-LinkNet) segmentation framework.<n>We also introduce a component-specific feature enhancement module (CSFEM) for a component-specific feature-enhanced classifier (CSFEC-Net)
  arXiv  Detail & Related papers  (2024-07-03T06:40:26Z)
• Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation [71.91773485443125]
  We show that the best AUC is achieved by the CDI$s$ - optimized modality, outperforming the best gold-standard modality by 0.0044. Notably, the optimized CDI$s$ modality also achieves AUC values over 0.02 higher than the Unoptimized CDI$s$ value.
  arXiv  Detail & Related papers  (2024-05-13T16:07:58Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
  Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:40:56Z)
• Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques [3.495246564946556]
  This paper introduces a methodology that combines automated image augmentation selection (RandAugment) with search strategies (Tree-based Parzen Estimator) We empirically validate our approach on breast cancer histology slides, focusing on the segmentation of cancer cells. Our results show that the proposed methodology leads to segmentation models that are more resilient to variations in histology slides.
  arXiv  Detail & Related papers  (2023-11-18T13:08:09Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.