Computer-aided diagnosis using embedded ensemble deep learning for multiclass drug-resistant tuberculosis classification

Abstract

IntroductionThis study aims to develop a web application, TB-DRD-CXR, for the categorization of tuberculosis (TB) patients into subgroups based on their level of drug resistance. The application utilizes an ensemble deep learning model that classifies TB strains into five subtypes: drug sensitive tuberculosis (DS-TB), drug resistant TB (DR-TB), multidrug-resistant TB (MDR-TB), pre-extensively drug-resistant TB (pre-XDR-TB), and extensively drug-resistant TB (XDR-TB).MethodsThe ensemble deep learning model employed in the TB-DRD-CXR web application incorporates novel fusion techniques, image segmentation, data augmentation, and various learning rate strategies. The performance of the proposed model is compared with state-of-the-art techniques and standard homogeneous CNN architectures documented in the literature.ResultsComputational results indicate that the suggested method outperforms existing methods reported in the literature, providing a 4.0%-33.9% increase in accuracy. Moreover, the proposed model demonstrates superior performance compared to standard CNN models, including DenseNet201, NASNetMobile, EfficientNetB7, EfficientNetV2B3, EfficientNetV2M, and ConvNeXtSmall, with accuracy improvements of 28.8%, 93.4%, 2.99%, 48.0%, 4.4%, and 7.6% respectively.ConclusionThe TB-DRD-CXR web application was developed and tested with 33 medical staff. The computational results showed a high accuracy rate of 96.7%, time-based efficiency (ET) of 4.16 goals/minutes, and an overall relative efficiency (ORE) of 100%. The system usability scale (SUS) score of the proposed application is 96.7%, indicating user satisfaction and a likelihood of recommending the TB-DRD-CXR application to others based on previous literature.

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

“…By optimizing the combination of outputs from various CNN architectures, these strategies ensure that the final disease classification leverages the strengths of each constituent model. Recent advancements in this area include metaheuristic approaches, such as differential evolution (DE) and particle swarm optimization (PSO), which have been adapted to optimize ensemble model weights for enhanced accuracy in disease detection [43].…”

“…This study contributes significantly to the advancement of leaf abnormality detection in agriculture. Through the introduction of new datasets and an extensive evaluation of various CNN architectures [43,[59][60][61], our research expands the scope of leaf disease research, particularly for C. asiatica leaves. The proposed ensemble deep learning model, coupled with innovative image segmentation and decision fusion strategies, presents a novel and effective approach to address the complexities of leaf abnormality classification.…”