This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.

A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after the trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.

In this study, a newly isolated Pediococcus acidilactici F3 was used as probiotic starter for producing fermented soymilk to enhance antioxidant properties with high antimicrobial activity against food-borne pathogens. The objectives of this study were to investigate optimized fermentation parameters of soymilk for enhancing antioxidant property by P. acidilactici F3 and to assess the dynamic antimicrobial activity of the fermented soymilk during co-culturing against candidate food-borne pathogens. Based on central composite design (CCD) methodology, the maximum predicted percentage of antioxidant activity was 78.9% DPPH inhibition. After model validation by a 2D contour plot, more suitable optimum parameters were adjusted to be 2% (v/v) inoculum and 2.5 g/L glucose incubated at 30 °C for 18 h. These parameters could provide the comparable maximum percentage of antioxidant activity at 74.5 ± 1.2% DPPH inhibition, which was up to a 23% increase compared to that of non-fermented soymilk. During 20 days of storage at 4 °C, antioxidant activities and viable cells of the fermented soymilk were stable while phenolic and organic contents were slightly increased. Interestingly, the fermented soymilk completely inhibited food-borne pathogens, Salmonella Typhimurium ATCC 13311, and Escherichia coli ATCC 25922 during the co-culture incubation. Results showed that the soymilk fermented by P. acidilactici F3 may be one of the alternative functional foods enriched in probiotics, and the antioxidation and antimicrobial activities may retain nutritional values and provide health benefits to consumers with high confidence.