Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Abstract

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 thae trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.

Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System

“…In our suggested model, these three CNN architectures have been uniformly incorporated. It has been demonstrated that the heterogeneous ensemble deep learning model is more effective than the heterogeneous deep learning model at classifying drug-resistant models [ 65 ], which is consistent with our finding that using the proposed model significantly improves solution quality, as shown in Table 2 , Table 3 , Table 4 , Table 5 and Table 6 .…”

“…This study employs UWA, AMIS-WCE, and AMIS-WLCE. The research demonstrated, in accordance with Han et al [ 16 ], Li et al [ 58 ] and Prasitpuriprecha et al [ 65 ], that the AMIS-WLCE improves the solution quality of the existing model.…”

“…The formula for calculating it is to divide the total number of guesses by the number of predictions that turned out to be accurate. Table 12 will show the efficiency of the previous research by collecting the AUC, F-measure, and accuracy of the selected previous research [ 65 ].…”

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

“…The artificial multiple intelligence systems (AMIS) method uses a system with many artificial multiple intelligences to assist in the identification of optimal solutions [42,51]. The system is called an intelligence box (IB), and it has algorithms with unique properties.…”

“…Therefore, machine learning was used to forecast the multiobjectives of weld seam properties and remains important when combined with AI for high performance. In [41,42], the authors demonstrated the application of AI using the "unlike entirety model" to categorize types of drug resistance in tuberculosis patients and to identify and classify unsavory images. Application of the unlike entirety AI model for forecasting mechanical properties should provide higher-quality solutions.…”