A Hybrid CNN–Transformer Framework with Wavelet-Based Feature Extraction for Multimodal Cancer Detection

Multi-class lung cancer detection and classification from CT scans using CNN transformer

Hybrid CNN-Transformer Framework for Multi-Disease Detection from Medical Imaging Data

Medical imaging plays an essential role in the early detection and clinical assessment of multiple diseases; however, manual image interpretation is time-consuming and can be affected by inter-observer variability, particularly when subtle pathological patterns are present. Conventional convolutional neural networks (CNNs) provide strong local feature extraction but may inadequately capture long-range spatial dependencies, whereas Vision Transformer-based architectures effectively model global contextual relationships but can require substantial training data. To exploit their complementary capabilities, this study proposes a Hybrid CNN-Transformer Framework for Multi-Disease Detection from Medical Imaging Data. The proposed architecture employs a multi-scale CNN backbone to extract local texture, boundary, morphological, and lesion-level characteristics, followed by Transformer-based self-attention to capture long-range dependencies among spatial feature representations. An attention-guided feature-fusion module integrates local CNN features with global Transformer representations, and the resulting discriminative embedding is processed by a multi-class classification layer for disease prediction. Data augmentation, class-aware training, and regularization are incorporated to improve robustness under heterogeneous medical-image distributions. Under the proposed experimental configuration, the hybrid framework achieves an overall accuracy of 96.74%, sensitivity of 95.92%, specificity of 97.18%, precision of 96.31%, F1-score of 96.11%, and area under the receiver operating characteristic curve (AUC) of 0.986. Compared with the selected standalone CNN baseline, the proposed approach provides approximately 5.2% relative improvement in accuracy and 5.8% improvement in F1-score. The combined local-global representation also improves discrimination of visually similar disease categories compared with individual CNN and Transformer models. These findings demonstrate the potential of hybrid CNN-Transformer architectures for robust and scalable computer-assisted multi-disease screening from medical imaging data. The proposed framework is intended to support clinical image assessment and prioritization rather than replace expert diagnosis.

A Novel Hybrid Deep Learning Architecture Combining CNNs, Vision Transformers, and Multi-Scale Attention for Enhanced Detection of Breast Cancer in Histopathology Images

Breast cancer has been one of the major causes of cancer mortality in the world. According to WHO report 2.5 million deaths are predicted as a result of breast cancer in the world in 2040. Although deep learning has demonstrated encouraging histopathology image analysis, the current methods frequently fail to provide local morphological information and global contextual information at the same time. In this paper, we present our HrybridViT-CAM, a hybrid deep learning system that integrates convolution neural networks( CNN ), Vision Transformers( ViT ) and multi-scale attention system in order to classify breast cancer using histopathology images. Its architecture has a two-way structure: a CNN arm (EfficientNetB7) to extract local features and a vision transformer arm to analyze the global context, fused together with a cross-attention fusion block. We use convolution block attention modules(CBAM) and deformal attention to improve feature discrimination. The model was tested on the BreaKHis dataset with various magnifications (40x, 100x, 200x, 400x) in binary as well as in multi-class classification. For binary classification (benign vs. malignant), HybridViT-CAM achieved accuracies of 99.87%, 99.42%, 98.95%, and 98.31% at 40χ, 100χ, 200x, and 400x magnifications, respectively. For eight-class subtype classification, the corresponding accuracies were 98.76%, 97.89%, 97.23%, and 96.54%, respectively. Grad-CAM++ and attention visualization techniques allowed explaining the results, which showed high correspondence(93.7% agreement) with pathologist diagnostic criterial. The proposed model was able to detect the malignant regions of interest (ROIs) like nuclei pleomorphism, atypia chromatin patterns and architectural distortions which are comparable to clinical diagnostic standards.

A CNN–Transformer Hybrid Model for Early Breast Cancer Detection from Mammograms

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 Dual-Stage CNN–Transformer Hybrid Network for Robust Feature Representation in Mammography and Ultrasound-Based Cancer Detection

Breast cancer is difficult to detect early and accurately with mammography and ultrasound images that are susceptible to high anatomical variability, low contrast, and noise. To overcome these issues, a novel Dual-Stage CNN–Transformer Hybrid Network is proposed in this study, which leverages the advantages of CNN to extract local spatial features and use transformer encoders to reason globally. In the first stage, a multi-scale CNN backbone is used to extract fine-grained morphological features like microcalcifications, lesions, and boundary textures. To capture long-range dependencies and cross-region interactions, a transformer-based encoder models long-range dependencies and cross-region interactions during the second stage, resulting in richer semantic understanding across heterogeneous tissue structures. A fusion module combines both spatial and context-specific representations, resulting in greater modality-invariant robustness against modality-specific artifacts. Experimental results conducted on the combined mammography and ultrasound datasets, show that the proposed hybrid network outperforms the state-of-the-art CNN-only and transformer-only models in terms of the sensitivity, specificity, and overall diagnostic accuracy. The findings suggest the promise of dual-stage hybrid systems for improving computer-aided diagnosis systems and assisting clinicians in accurate breast cancer screening.

Accurate Brain Tumor Classification Using MRI Images Based on A Hybrid Vision Transformer and BiLSTM Framework

Results show that ViT–BiLSTM's classification performance is superior to those of traditional deep learning methods: among all the tumor categories its accuracy is higher, its fine-tuning more perfect, as well as, its Recall rates greater.