In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

Currently, many deep learning models are being used to classify COVID‐19 and normal cases from chest X‐rays. However, the available data (X‐rays) for COVID‐19 is limited to train a robust deep‐learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high‐dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep‐convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID‐19). To validate whether the generated images are accurate, we used the k‐mean clustering technique with three clusters (Normal, Pneumonia, and COVID‐19). We only selected the X‐ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X‐rays, we used the Grad‐CAM technique to visualize the underlying pattern, which leads the network to its final decision.

To screen and detect the harmful substance melamine (MEL), a quantum-dot-bead-based immunochromatographic assay (QB-ICA) was formulated. After optimization, calibration was performed within the linear range from 0.06 to 0.28 ng mL⁻¹, with limit of detection (LOD) of 0.04 ng mL⁻¹. The LOD was 35 times lower than that of ICA that used colloidal gold nanoparticles (LOD = 1.4 ng mL⁻¹) and 40 times lower than that of the assay based on quantum dots (LOD = 1.6 ng mL⁻¹). In the detection of MEL in spiked pure milk using the proposed QB-ICA strategy, the LOD (LOD = 0.19 ng mL⁻¹) of the samples with the proposed pretreatment was 18.4 times lower than those of the samples without pretreatment (LOD = 3.5 ng mL⁻¹). The performance and practicability of the proposed QB-ICA system was validated; the obtained results reveal that QB-ICA is comparable with the conventional enzyme-linked immunosorbent assay (ELISA) method, but with enhanced applicability. Given its high sensitivity and practicability, the QB-ICA strategy could become a worthwhile alternative for the rapid, sensitive, and quantitative onsite detection of harmful substances, facilitating food safety monitoring.