Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 22 references
Abstract
Breast cancer diagnosis using mammography remains a challenging task, particularly in the presence of dense and heterogeneous tissue structures. While deep learning (DL) methods have shown promise, their interpretability and performance are often constrained by suboptimal configurations and limited contextual learning. This paper introduces a Vision Transformer (ViT)-based model, enhanced by the Reptile Search Algorithm (RSA), to achieve both accurate classification and explainability. The ViT framework captures global dependencies in mammographic image patches, while RSA optimizes critical hyperparameters, including patch size, learning rate, and attention depth, to enhance model convergence and generalization. Applied on the CBIS-DDSM: Breast Cancer Image Dataset, the ViT-RSA model attains a classification accuracy of 96.2% and an Area Under the Curve (AUC) of 0.97. Interpretability is ensured through attention visualization maps from ViT layers and Grad-Cam-based explanations. The experimental results confirm that ViT-RSA outperforms conventional CNN models, offering a robust and interpretable tool for breast cancer diagnosis that supports clinical decision-making.
It is suggested that integrating advanced CNN architectures with radiomics features can significantly enhance the accuracy and reliability of both lesion segmentation and treatment prediction in breast cancer, potentially leading to better clinical outcomes.
Astha Karnwal, E. Dhamija, Pradeeba Sridar· Indian Journal of Radiology...· 0 citations
This work presents a leakage-controlled deep-learning framework for breast cancer classification using the CBIS-DDSM mammography archive that combines patient-level data partitioning before augmentation, a two-stage transfer-learning strategy based on ResNet50, and an Inter-View Attention Fusion (IVAF) module for adapt...
Magy Makram, Alber S. Aziz, Mary Monir Saeid et al.· Scientific Reports· 0 citations
Results demonstrate that CNN-based architectures achieve robust performance in capturing local spatial features, while transformer-based models provide competitive results through global contextual representation, however, variations are observed in model calibration and false-negative rates, which are critical for cli...
The integration of these advanced techniques significantly enhances diagnostic reliability, addressing the challenges in histopathological image analysis, and proposes a novel explainable multi-model DL framework for breast cancer classification leveraging histopathological images.
Muhammad Nabeel Mehmood, Muhammad Hassaan Ashraf· Informatica· 0 citations
Early-stage identification of breast cancer is imperative in decreasing the cancer-related mortality rate. However, the interpretation of mammograms is difficult as the contrast is low, and lesions of interests may be subtle. This study proposes a hybrid model which combines Convolutional Neural Networks (CNN) and Tran...
A. Sengar, Deepika Dattatraya Walanjkar, V. Pushpa et al.· 2026 7th International Confe...· 0 citations
Early detection of breast cancer through mammograms is a difficult process since mammograms have a poor contrast ratio and contain noise. Though U-Net architectures have provided good results for image processing in medicine, their performance is greatly affected by poor feature extraction and lack of integration with...
Monalisa Ghosh, Satyakam Baraha· International Conference on...· 0 citations
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