Background: This study proposes a deep learning-based decision support system for the non-invasive prediction of axillary lymph node (ALN) metastasis in breast cancer using ultrasound (USG). Both localisation and benign–malignant classification of ALNs were performed using YOLO-v11, a single-stage object detection approach. The dataset created from labelled USG images was split into training/validation/test sets at a ratio of 70%/20%/10%, and the model produced a class label and confidence score for each prediction. Performance was found to be high with amAP@0.5 = 0.904s (benign: 0.866, malignant: 0.942); F1-, precision-, and sensitivity-confidence curves showed that thresholds in the 0.52–0.60 range provided a balance between sensitivity and specificity. Confidence scores for the malignant class were observed to be concentrated in the 0.82–0.87 band. The confusion matrix includes an auxiliary ‘background’ row/column representing unmatched predictions (false positives) and missed ground-truth detections (false negatives); background was not modelled as a semantic class during training. The findings point to a rapid and reproducible artificial intelligence approach that could reduce operator dependency in USG. The proposed model has the potential to provide radiologists and clinicians with non-invasive, real-time decision support in ALN assessment. Objective: Axillary lymph node (ALN) metastasis plays a decisive role in the clinical course and treatment planning of breast cancer following diagnosis. Therefore, the accurate and early detection of metastatic lymph nodes directly impacts treatment success. Ultrasonography (USG) stands out as the most common and effective imaging method for evaluating axillary lymph node metastasis. The main objective of this study is to develop an artificial intelligence-supported USG analysis system to provide a decision support system for radiologists and clinicians in the detection of ALN metastasis. Methods: This study included a total of 471 patients: 248 patients diagnosed with breast cancer between 2024 and 2025 and reported as malignant based on axillary lymph node biopsy results, and 223 control cases with benign lymph nodes detected in breast ultrasound scans. Using a dataset created from the acquired ultrasound images, a single-stage object detection model (YOLO-v11) was employed to detect ALN metastasis. The data were split into a 70% training, 20% validation, and 10% testing ratio. The developed model produces a confidence score and class label for each detection. Results: The developed model has demonstrated high accuracy performance in benign and malignant lymph node samples. The mAP@0.5 value calculated under the Precision–Recall (PR) curve was obtained as 0.904 (benign: 0.866, malignant: 0.942). The precision and sensitivity values were found to be 0.866 in benign samples and 0.942 in malignant samples, respectively. Confidence score values for the malignant class were concentrated in the range of 0.82–0.87. When evaluated from a clinical application perspective, the threshold value in the range of 0.5–0.6 provides a balanced trade-off between sensitivity and specificity. Conclusion: The proposed artificial intelligence model can localise axillary lymph nodes with high accuracy on USG images and distinguish between benign and malignant conditions. In this respect, the model has the potential to guide radiologists and clinicians as a decision support tool in the diagnostic process.

Broccoli juice is highly nutritious, containing phenolic compounds, chlorophyll derivatives, antioxidants, and other biologically active phytochemicals; however, conventional thermal processing may reduce their stability and bioaccessibility. The aim of this study was to analyse the impact of thermosonication on the following properties of broccoli juice: bioactivity, antioxidant capacity, phenolic profile and in vitro bioaccessibility. Thermosonication conditions were optimized using Response Surface Methodology (RSM) integrated with the Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms. Broccoli juice samples were subjected to conventional pasteurization and thermosonication under different processing conditions, and total chlorophyll, ascorbic acid (AA), ferric reducing antioxidant power (FRAP), total phenolic content (TPC), and individual phenolic compounds were evaluated. Digestive stability and post-digestion bioaccessibility were also assessed using a standardized in vitro gastrointestinal digestion model. The developed quadratic models exhibited high statistical significance for TPC and FRAP responses ( p < 0.001), with coefficients of determination ( R ² ) of 0.9949 and 0.9853, respectively. The optimum thermosonication conditions were identified as 40 °C, 6.75 min, and 64.04% amplitude, under which experimentally validated FRAP and TPC values reached 12.86 mmol TE/L and 149.77 mg GAE/100 ml, respectively. HPLC-DAD analysis showed that gallic acid and naringin were the pre-dominant phenolic compounds, and that thermosonication promoted greater phenolic retention than conventional pasteurization. In vitro digestion results demonstrated that thermosonicated broccoli juice maintained significantly higher levels of TPC, FRAP, chlorophyll, and AA throughout gastrointestinal digestion, with higher TPC recovery (30.45%) than pasteurized samples (26.22%), indicating improved post-digestion bioaccessibility of phenolic compounds. Similar improvements were observed for antioxidant capacity and chlorophyll retention. Furthermore, PSO and GWO optimization results showed strong agreement with RSM predictions, confirming the robustness of the proposed optimization strategy. Overall, thermosonication was shown to effectively and sustainably enhance the functional quality, antioxidant properties, phenolic stability, and digestive bioaccessibility of broccoli juice.

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology.