Breast cancer is ranked as the second most common cancer among women globally, highlighting the critical need for precise and early detection methods. Our research introduces a novel approach for classifying benign and malignant breast ultrasound images. We leverage advanced deep learning methodologies, mainly focusing on the vision transformer (ViT) model. Our method distinctively features progressive fine‐tuning, a tailored process that incrementally adapts the model to the nuances of breast tissue classification. Ultrasound imaging was chosen for its distinct benefits in medical diagnostics. This modality is noninvasive and cost‐effective and demonstrates enhanced specificity, especially in dense breast tissues where traditional methods may struggle. Such characteristics make it an ideal choice for the sensitive task of breast cancer detection. Our extensive experiments utilized the breast ultrasound images dataset, comprising 780 images of both benign and malignant breast tissues. The dataset underwent a comprehensive analysis using several pretrained deep learning models, including VGG16, VGG19, DenseNet121, Inception, ResNet152V2, DenseNet169, DenseNet201, and the ViT. The results presented were achieved without employing data augmentation techniques. The ViT model demonstrated robust accuracy and generalization capabilities with the original dataset size, which consisted of 637 images. Each model’s performance was meticulously evaluated through a robust 10‐fold cross‐validation technique, ensuring a thorough and unbiased comparison. Our findings are significant, demonstrating that the progressive fine‐tuning substantially enhances the ViT model’s capability. This resulted in a remarkable accuracy of 94.49% and an AUC score of 0.921, significantly higher than models without fine‐tuning. These results affirm the efficacy of the ViT model and highlight the transformative potential of integrating progressive fine‐tuning with transformer models in medical image classification tasks. The study solidifies the role of such advanced methodologies in improving early breast cancer detection and diagnosis, especially when coupled with the unique advantages of ultrasound imaging.

Breast cancer significantly affects people’s health globally, making early and accurate diagnosis vital. While ultrasound imaging is safe and non-invasive, its manual interpretation is subjective. This study explores machine learning (ML) techniques to improve breast ultrasound image segmentation, comparing models trained on combined versus separate classes of benign and malignant tumors. The YOLOv8 object detection algorithm is applied to the image segmentation task, aiming to capitalize on its robust feature detection capabilities. We utilized a dataset of 780 ultrasound images categorized into benign and malignant classes to train several deep learning (DL) models: UNet, UNet with DenseNet-121, VGG16, VGG19, and an adapted YOLOv8. These models were evaluated in two experimental setups—training on a combined dataset and training on separate datasets for benign and malignant classes. Performance metrics such as Dice Coefficient, Intersection over Union (IoU), and mean Average Precision (mAP) were used to assess model effectiveness. The study demonstrated substantial improvements in model performance when trained on separate classes, with the UNet model’s F1-score increasing from 77.80 to 84.09% and Dice Coefficient from 75.58 to 81.17%, and the adapted YOLOv8 model achieving an F1-score improvement from 93.44 to 95.29% and Dice Coefficient from 82.10 to 84.40%. These results highlight the advantage of specialized model training and the potential of using advanced object detection algorithms for segmentation tasks. This research underscores the significant potential of using specialized training strategies and innovative model adaptations in medical imaging segmentation, ultimately contributing to better patient outcomes.

Background/Objectives: The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these nerve cells grow rapidly and abnormally, it can lead to the development of a brain tumor. Brain tumors are severe conditions that can significantly reduce a person’s lifespan. Failure to detect or delayed diagnosis of brain tumors can have fatal consequences. Accurately identifying and classifying brain tumors poses a considerable challenge for medical professionals, especially in terms of diagnosing and treating them using medical imaging analysis. Errors in diagnosing brain tumors can significantly impact a person’s life expectancy. Magnetic Resonance Imaging (MRI) is highly effective in early detection, diagnosis, and classification of brain cancers due to its advanced imaging abilities for soft tissues. However, manual examination of brain MRI scans is prone to errors and heavily depends on radiologists’ experience and fatigue levels. Swift detection of brain tumors is crucial for ensuring patient safety. Methods: In recent years, computer-aided diagnosis (CAD) systems incorporating deep learning (DL) and machine learning (ML) technologies have gained popularity as they offer precise predictive outcomes based on MRI images using advanced computer vision techniques. This article introduces a novel hybrid CAD approach named ViT-PCA-RF, which integrates Vision Transformer (ViT) and Principal Component Analysis (PCA) with Random Forest (RF) for brain tumor classification, providing a new method in the field. ViT was employed for feature extraction, PCA for feature dimension reduction, and RF for brain tumor classification. The proposed ViT-PCA-RF model helps detect early brain tumors, enabling timely intervention, better patient outcomes, and streamlining the diagnostic process, reducing patient time and costs. Our research trained and tested on the Brain Tumor MRI (BTM) dataset for multi-classification of brain tumors. The BTM dataset was preprocessed using resizing and normalization methods to ensure consistent input. Subsequently, our innovative model was compared against traditional classifiers, showcasing impressive performance metrics. Results: It exhibited outstanding accuracy, specificity, precision, recall, and F1 score with rates of 99%, 99.4%, 98.1%, 98.1%, and 98.1%, respectively. Conclusions: Our innovative classifier’s evaluation underlined our model’s potential, which leverages ViT, PCA, and RF techniques, showing promise in the precise and effective detection of brain tumors.

In this study, we introduce a novel combination of layer-static-weighted attention and ascending feature selection techniques to predict the seriousness level of adverse drug events using the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We utilized natural language processing (NLP) to analyze the terms in the active substance field, in addition to considering demographic and event information such as patient sex, healthcare provider qualification, and drug characterization. Our ascending feature selection method, which progressively incorporates additional features based on their importance, demonstrated continuous enhancements in prediction performance. Simultaneously, we employed a layer-static-weighted attention technique, which dynamically adjusts the model’s focus between natural language processing (NLP) and demographic features. This technique achieved its best performance at a balanced weight of 50%, yielding an average test accuracy of 74.56% and CV ROC score of 0.83 when 4000 features were included, indicating a compelling advantage to include a larger volume of meaningful features. By integrating these methodologies, we constructed a robust model capable of effectively predicting seriousness levels, offering significant potential for improving pharmacovigilance and enhancing drug safety monitoring. The results underscore the value of NLP and demographic data in predicting drug event seriousness and demonstrate the effectiveness of our combined techniques. We encourage further research to refine these methods and evaluate their application to other clinical datasets.