The detection of fish freshness is crucial for ensuring food safety. This study addresses the limitations of traditional detection methods, which rely on laboratory equipment and complex procedures, by proposing a smartphone-based detection method, termed FreshFusionNet, that utilizes a pitaya peel pH intelligent indicator film in conjunction with multimodal deep learning. The pitaya peel indicator film, prepared using high-pressure homogenization technology, demonstrates a significant color change from dark red to yellow in response to the volatile alkaline substances released during fish spoilage. To construct a multimodal dataset, 3600 images of the indicator film were captured using a smartphone under various conditions (natural light and indoor light) and from multiple angles (0° to 120°), while simultaneously recording pH values, total volatile basic nitrogen (TVB-N), and total viable count (TVC) data. Based on the lightweight MobileNetV2 network, a Multi-scale Dilated Fusion Attention module (MDFA) was designed to enhance the robustness of color feature extraction. A Temporal Convolutional Network (TCN) was then used to model dynamic patterns in chemical indicators across spoilage stages, combined with a Context-Aware Gated Fusion (CAG-Fusion) mechanism to adaptively integrate image and chemical temporal features. Experimental results indicate that the overall classification accuracy of FreshFusionNet reaches 99.61%, with a single inference time of only 142 ± 40 milliseconds (tested on Xiaomi 14). This method eliminates the need for professional equipment and enables real-time, non-destructive detection of fish spoilage through smartphones, providing consumers and the food supply chain with a low-cost, portable quality-monitoring tool, thereby promoting the intelligent and universal development of food safety detection technology.

Background: Pneumonia is considered one of the most important causes of morbidity and mortality in the world. Bacterial and viral pneumonia share many similar clinical features, thus making diagnosis a challenging task. Traditional diagnostic method developments mainly rely on radiological imaging and require a certain degree of consulting clinical experience, which can be inefficient and inconsistent. Deep learning for the classification of pneumonia in multiple modalities, especially integrating multiple data, has not been well explored.Methods: The study introduce the PneumoFusion-Net, a deep learning-based multimodal framework that incorporates CT images, clinical text, numerical lab test results, and radiology reports for improved diagnosis. In the experiments, a dataset of 10,095 pneumonia CT images was used-including associated clinical data-most of which was used for training and validation while keeping part of it for validation on a held-out test set. Five-fold cross-validation was considered in order to evaluate this model, calculating different metrics including accuracy and F1-Score.Results: PneumoFusion-Net, which achieved 98.96% classification accuracy with a 98% F1-score on the held-out test set, is highly effective in distinguishing bacterial from viral types of pneumonia. This has been highly beneficial for diagnosis, reducing misdiagnosis and further improving homogeneity across various data sets from multiple patients.Conclusion: PneumoFusion-Net offers an effective and efficient approach to pneumonia classification by integrating diverse data sources, resulting in high diagnostic accuracy. Its potential for clinical integration could significantly reduce the burden of pneumonia diagnosis by providing radiologists and clinicians with a robust, automated diagnostic tool.

Background: Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) represent the primary liver cancer types. Traditional diagnostic techniques, reliant on radiologist interpretation, are both time-intensive and often inadequate for detecting the less prevalent CCA. There is an emergent need to explore automated diagnostic methods using deep learning to address these challenges.Methods: This study introduces HTRecNet, a novel deep learning framework for enhanced diagnostic precision and efficiency. The model incorporates sophisticated data augmentation strategies to optimize feature extraction, ensuring robust performance even with constrained sample sizes. A comprehensive dataset of 5,432 histopathological images was divided into 5,096 for training and validation, and 336 for external testing. Evaluation was conducted using five-fold cross-validation and external validation, applying metrics such as accuracy, area under the receiver operating characteristic curve (AUC), and Matthews correlation coefficient (MCC) against established clinical benchmarks.Results: The training and validation cohorts comprised 1,536 images of normal liver tissue, 3,380 of HCC, and 180 of CCA. HTRecNet showed exceptional efficacy, consistently achieving AUC values over 0.99 across all categories. In external testing, the model reached an accuracy of 0.97 and an MCC of 0.95, affirming its reliability in distinguishing between normal, HCC, and CCA tissues.Conclusion: HTRecNet markedly enhances the capability for early and accurate differentiation of HCC and CCA from normal liver tissues. Its high diagnostic accuracy and efficiency position it as an invaluable tool in clinical settings, potentially transforming liver cancer diagnostic protocols. This system offers substantial support for refining diagnostic workflows in healthcare environments focused on liver malignancies.