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. The proposed pipeline 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 adaptive fusion of paired craniocaudal (CC) and mediolateral oblique (MLO) feature maps. IVAF was modeled as a light-weighted convolutional gating strategy added after the last ResNet50 convolutional layer in order to create a weighted spatial-channel representation from the paired mammography images. In terms of the performance of the model under CBIS-DDSM held-out testing protocol, the entire model scored an accuracy of 97.12%, sensitivity of 96.44%, specificity of 97.68%, and AUC-ROC of 0.9876 based on the test results obtained on 6,117 images of 222 different patients. The average accuracy obtained using 100 random seeds was found to be 97.11% ± 0.18%.
Magy Makram, Alber S. Aziz, Mary Monir Saeid et al.· Scientific Reports· 0 citations
Early and accurate detection of skin cancer, particularly melanoma, remains a critical challenge in computer-aided diagnosis, motivating the development of reliable and interpretable machine learning solutions. This study presents a comparative algorithmic analysis of deep learning models for automated skin cancer detection using dermoscopic images. Specifically, convolutional neural networks (CNNs) and Vision Transformers (ViTs) are implemented within a unified framework, employing transfer learning and standardized preprocessing techniques on a benchmark dataset. The proposed methodology incorporates data augmentation and class imbalance handling strategies, while model performance is evaluated using clinically relevant metrics, including accuracy, precision, recall, F1-score, and area under the ROC curve. In addition, explainability techniques such as Grad-CAM and attention visualization are employed to enhance model interpretability, and decision threshold analysis is conducted to assess trade-offs between sensitivity and specificity in melanoma detection. Experimental 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 clinical deployment. Overall, the findings highlight the importance of combining algorithmic performance with interpretability and threshold optimization to support reliable and clinically meaningful computer-aided diagnosis systems.
Breast cancer is a significant cause of cancer-related deaths among women worldwide. Its early identification and screening are essential for improved patient outcomes and reduced mortality rates. Histopathological image analysis is considered as the gold standard for the diagnosis and prognosis of breast cancer. Nevertheless, the complexity of Whole-Slide Images (WSI) and their manual examination make this task time consuming, and prone to pathologist subjectivity. Recently, Deep Learning (DL) technology has achieved remarkable success in computer vision. However, their application still faces critical challenges in pathology analysis, including Region-of-Interest (RoI) scale variations, inter- and intra-class heterogeneity, diverse staining protocols, and the scarcity of annotated datasets. Furthermore, DL model’s findings are opaque and lack decision-level transparency. This study proposes a novel explainable multi-model DL framework for breast cancer classification leveraging histopathological images. The framework integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) for image contrast enhancement, and diverse data augmentation to mitigate class imbalance and overfitting. Proposed architecture ensembles two branches, one employes DenseNet201 benefiting from Transfer Learning (TL) via ImageNet weights, while other utilizes a custom light weight attention based Hierarchal Feature Fusion (HFF) Network. DenseNet201 utilizes multilevel features to effectively tackle gradient vanishing issues and capture intricate feature representations, while HFF-Net, designed specifically for biomedical imaging, leverages HFF stem and multiscale feature extraction with Swish activation to enhance learning stability. Attention mechanism introduced within HFF-Net further refines the output features. Final feature vectors from the two branches are fused at the Global Average Pooling (GAP) layer, consolidating discriminative information. Experimental results on BRACS dataset demonstrate the proposed framework achieves 97.15% accuracy, 92.59% precision, 93.81% recall, and a 93.19% F1-score in screening tasks, while for grading tasks, it attains 84.08% accuracy, 83.39% precision, 83.64% recall, and 83.44% F1-score on 4391 test samples. Additionally, Gradient-Class Activation Mapping (Grad-CAM) saliency heatmap are generated for visual representation of proposed model’s choices, thereby increased transparency. The integration of these advanced techniques significantly enhances diagnostic reliability, addressing the challenges in histopathological image analysis.
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 Transformers, to provide an accurate and timely mechanism for the detection of breast cancer. The hybrid model captures the CNN’s ability to identify local features (e.g., edges and textures) and the Transformer’s ability to identify global features and context through self-attention. The model is implemented and validated on the MIAS and CBIS-DDSM datasets. The proposed method utilizes CLAHE for noise and contrast enhancement and captures features more effectively. The hybrid model also provides an improvement in Confirmed Contrast Enhancement (CCE) of 3.2 and greater entropy values when compared to baseline CNN models, ResNet and DenseNet. The results of the study indicate that the integration of CNN and Transformer, improves the reliability of the diagnosis, and supports the use of the model for the further development of a Real-time Clinical Decision Support (CDS) System for the early detection of breast cancer.
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 real-time diagnostics. This paper puts forward an attention-assisted multi-task U-Net architecture which aids in detecting breast cancer lesions along with their real-time risk estimation. Attention has been applied within the architecture to make it learn features more accurately and localize suspicious regions, whereas the use of a multi-task approach allows the model to detect lesions as well as assess their risk. Moreover, a pipeline has been devised to facilitate real-time interaction with the system using mammograms. The developed model is trained and tested using benchmark mammography datasets and attains a classification accuracy of 97.5%, along with higher robustness than existing CNN-based models. The experimental findings demonstrate greater reliability in terms of cancer detection, especially during its early stages. The designed framework offers a practical and affordable approach to breast cancer screening by connecting detection using deep learning algorithms with real-time decision-making.
Monalisa Ghosh, Satyakam Baraha· International Conference on...· 0 citations
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