Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: Approach A (i.e., binary classification) and Approach B (i.e., multi-classification). To build the proposed CAD system, the benchmark public multi-class INbreast dataset is used. Meanwhile, private real breast cancer images are collected and annotated by expert radiologists to validate the prediction performance of the proposed ETECADx framework. The promising evaluation results are achieved using the INbreast mammograms with overall accuracies of 98.58% and 97.87% for the binary and multi-class approaches, respectively. Compared with the individual backbone networks, the proposed ensemble learning model improves the breast cancer prediction performance by 6.6% for binary and 4.6% for multi-class approaches. The proposed hybrid ETECADx shows further prediction improvement when the ViT-based ensemble backbone network is used by 8.1% and 6.2% for binary and multi-class diagnosis, respectively. For validation purposes using the real breast images, the proposed CAD system provides encouraging prediction accuracies of 97.16% for binary and 89.40% for multi-class approaches. The ETECADx has a capability to predict the breast lesions for a single mammogram in an average of 0.048 s. Such promising performance could be useful and helpful to assist the practical CAD framework applications providing a second supporting opinion of distinguishing various breast cancer malignancies.

Breast cancer, which attacks the glandular epithelium of the breast, is the second most common kind of cancer in women after lung cancer, and it affects a significant number of people worldwide. Based on the advantages of Residual Convolutional Network and the Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes a novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While the backbone residual deep learning network is employed to create the deep features, the transformer is utilized to classify breast cancer according to the self-attention mechanism. The proposed CAD system has the capability to recognize breast cancer in two scenarios: Scenario A (Binary classification) and Scenario B (Multi-classification). Data collection and preprocessing, patch image creation and splitting, and artificial intelligence-based breast lesion identification are all components of the execution framework that are applied consistently across both cases. The effectiveness of the proposed AI model is compared against three separate deep learning models: a custom CNN, the VGG16, and the ResNet50. Two datasets, CBIS-DDSM and DDSM, are utilized to construct and test the proposed CAD system. Five-fold cross validation of the test data is used to evaluate the accuracy of the performance results. The suggested hybrid CAD system achieves encouraging evaluation results, with overall accuracies of 100% and 95.80% for binary and multiclass prediction challenges, respectively. The experimental results reveal that the proposed hybrid AI model could identify benign and malignant breast tissues significantly, which is important for radiologists to recommend further investigation of abnormal mammograms and provide the optimal treatment plan.

Valvular heart disease (VHD) is one of the primary causes of cardiovascular illnesses with high mortality rates worldwide. Early detection of VHD enables optimal treatment and stops the onset of heart problems. Even with the capability of diagnosing VHD, many misdiagnosed cases have existed. Therefore, in this work, four AI models are proposed to effectively diagnose heart sounds generated from phonocardiography (PCG) recordings, namely VGG16, ResNet50, Transformer Encoder, and a hybrid framework based on ResNet50 and the Transformer Encoder with Multiple Layer Perceptron (MLP). Two benchmark datasets are involved, namely Yaseen and PhysioNet 2016 challenge datasets. The suggested AI models can classify heart sounds in two scenarios: Multi-class scenario (aortic stenosis, mitral stenosis, mitral regurgitation, mitral valve prolapse, and normal) and binary classification (normal and abnormal). Melfrequency cepstral coefficients (MFCCs) features are extracted from heart sound files, where a 22050-sample rate is used with 12 seconds as the maximum clip duration for each audio file. Additionally, a pre-processing technique is applied using the z-score normalization and Discrete cosine transform (DCT) algorithms. Besides, noise, stretching, and shifting are used to augment sound data. By using a 5-fold cross-validation technique, the proposed hybrid framework has outperformed other models using the Yaseen dataset, reaching an average of 99.80% for accuracy and AUC, respectively. On the other hand, this model can achieve averages of 97.40% and 96.40% for accuracy and AUC, respectively, when the PhysioNet 2016 challenge dataset is used. When the Yaseen dataset is relabeled as normal and abnormal, it reaches 100.0% for both accuracy and AUC. The experiment’s findings indicate that the proposed hybrid AI model can significantly recognize heart sound recordings. This ability is essential for the domain of experts to advise the most effective treatment and prompt further investigation into suspicious cases.

Parkinson’s disease is a progressive neurological condition that affects motor abilities. Common symptoms include tremors, muscle stiffness, and difficulty with coordinated movements. A variety of efforts are underway to address these issues and improve diagnostic precision in Parkinson’s disease. This paper employs well-known machine-learning techniques to improve diagnostic accuracy. A variety of individual and ensemble AI models have been proposed, including Random Forest, Decision Tree, Logistic Regression, Gradient Boosting, Support Vector Machine, Stacking, and Bagging Ensemble classifiers. Three scenarios are applied to two standard benchmark datasets. The best performance is achieved when the Stacking Ensemble classifier is utilized, where the Support Vector Machine and Gradient Boosting are engaged for extracting features and Logistic Regression for classifying Parkinson’s disease. 1.00,0.00,0.00The Stacking Ensemble classifier reaches 94.87% accuracy and 90.00% AUC for the first dataset, while for the second dataset, 96.18% accuracy and 96.27% AUC are recorded. The final results demonstrate the importance of the suggested framework, which can help to improve the overall diagnosis outcomes.

Background/Objectives: Early and accurate detection of breast cancer is essential for reducing mortality and improving patient outcomes. However, the manual interpretation of breast ultrasound images is challenging due to image variability, noise, and inter-observer subjectivity. This study aims to address these limitations by developing an automated and interpretable computer-aided diagnosis (CAD) system. Methods: We propose an automated and interpretable computer-aided diagnosis (CAD) system that integrates ensemble transfer learning with Vision Transformer architectures. The system combines the Data-Efficient Image Transformer (Deit) and Vision Transformer (ViT) through concatenation-based feature fusion to exploit their complementary representations. Preprocessing, normalization, and targeted data augmentation enhance robustness, while Gradient-weighted Class Activation Mapping (Grad-CAM) provides visual explanations to support clinical interpretability. The proposed model is benchmarked against state-of-the-art CNNs (VGG16, ResNet50, DenseNet201) and Transformer models (ViT, DeiT, Swin, Beit) using the Breast Ultrasound Images (BUSI) dataset. Results: The ensemble achieved 96.92% accuracy and 97.10% AUC for binary classification, and 94.27% accuracy with 94.81% AUC for three-class classification. External validation on independent datasets demonstrated strong generalizability, with 87.76%/88.07% accuracy/AUC on BrEaST, 86.77%/85.90% on BUS-BRA, and 86.99%/86.99% on BUSI_WHU. Performance decreased for fine-grained BI-RADS classification—76.68%/84.59% accuracy/AUC on BUS-BRA and 68.75%/81.10% on BrEaST—reflecting the inherent complexity and subjectivity of clinical subclassification. Conclusions: The proposed Vision Transformer-based ensemble demonstrates high diagnostic accuracy, strong cross-dataset generalization, and clinically meaningful explainability. These findings highlight its potential as a reliable second-opinion CAD tool for breast cancer diagnosis, particularly in resource-limited clinical environments.

This dataset was specifically collected to support the development and evaluation of computer-aided diagnosis (CAD) systems for breast cancer detection. Th