Aug 2026· International Journal of AI Electronics and Nexus Energy· 0 citations· 3 references
TL;DR
A deep learning-based system that uses chest X-ray pictures to automatically detect tuberculosis, using transfer learning using MobileNetV2 and DenseNet architectures to classify chest Xrays as either TB-positive or Healthy, reaching notable accuracy.
Abstract
One of the most common and deadly infectious illnesses in the world is still tuberculosis (TB), especially in developing nations with inadequate healthcare systems. In order to stop the spread of tuberculosis and enhance patient outcomes, early identification and diagnosis are essential. In this study, we present a deep learning-based system that uses chest X-ray pictures to automatically detect tuberculosis. Despite the difficulties of limited dataset availability, the system uses transfer learning using MobileNetV2 and DenseNet architectures to classify chest Xrays as either TB-positive or Healthy, reaching notable accuracy. To increase model generalisation and image quality, pre-processing methods like Contrast Limited Adaptive Histogram Equalisation (CLAHE) and sophisticated data augmentation approaches are used. The trained model is then implemented as a Flask web application, offering a user-friendly interface with features like secure login, image upload and preview, prediction results with probability scores, and performance metrics visualisation like accuracy curves, confusion matrices, and ROC curves. The suggested framework shows how deep learning can be used to create scalable, dependable, and affordable diagnostic tools to help radiologists and other medical professionals with TB screening and diagnosis.
The rising incidence of pneumonia and tuberculosis presents major public health challenges, particularly in resource-limited regions such as Ethiopia, where shortages of skilled radiologists hinder timely diagnosis. Although deep learning has shown promise in medical image analysis, robust multiclass classification of these diseases from chest X-ray images remains technically challenging. This study proposes a deep learning–based multiclass classification framework for automated detection of pneumonia and tuberculosis from chest X-rays. A total of 2,100 chest X-ray images were collected from major hospitals in the Amhara region. Images were preprocessed and augmented to improve robustness. Multiple convolutional neural network architectures including Sequential CNN, VGG19, MobileNetV3-Large, EfficientNetV2-B0/B1, and InceptionResNet were trained and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. A two-stage transfer learning strategy with systematic hyperparameter optimization was applied. Among the evaluated models, the customized VGG19 achieved the best performance, with 99% accuracy, precision, recall, and F1-score, and an AUC of 99.93%. Other models also demonstrated strong performance, with accuracies ranging from 95 to 98%. These results highlight the importance of model selection and optimization in medical image classification. An independent external validation using 3,568 chest X-ray images demonstrated robust generalization, with an accuracy of 88.83% and macro F1-score of 83.92%. Future work will focus on prospective multicenter validation, patient-level evaluation, and expert radiologist verification of the explainability framework.
Addisu Baye Flatie, Zegeye Regasa Wordofa, Abraham Keffale Mengistu et al.· Discover Artificial Intellig...· 0 citations
The proposed deep learning models provide an efficient and accurate tool for multiclass pneumonia detection from CXR images and have the potential to support healthcare professionals in making more accurate diagnoses.
Timothy Karani, Stephen Waithaka· Journal of the Kenya Nationa...· 0 citations
A hybrid ensemble learning approach to classify chest X-ray images into four classes—Normal, COVID-19, Pneumonia, and Tuberculosis exhibited high sensitivity in detecting Tuberculosis with considerable stability in classifying Normal, COVID-19, and Pneumonia.
Abdul Rehman Khan Tareen, Muhammad Laiq Ur Rahman Shahid, Muhammad Hamza Zafar et al.· Allied Medical Research Jour...· 0 citations
A Hybrid Prototype Fusion Network (HPFN) developed based on the ConvNeXt-Tiny architecture is proposed, and deep feature representation and prototype-based similarity calculation are combined in a single decision mechanism, outperforming both the compared deep learning architectures and traditional feature-based methods.
Pulmonary tuberculosis remains a major public health issue in Indonesia, intensified by a shortage of radiologists for early detection. Although deep learning enables automated screening, most current models focus on binary classification and often miss post-TB sequelae, a critical factor in elimination strategies. This study aimed to evaluate the clinical implementation of a deep learning model based on the Xception architecture for multiclass tuberculosis detection in chest X-ray (CXR) images, including normal, active, and post-TB sequelae tuberculosis. This analytical observational study evaluated a prospective implementation cohort of 529 adult chest X-ray images from five regions in Central Java, Indonesia, comprising 259 normal, 246 active TB, and 24 post-TB sequelae cases. The Xception model was developed using a separate dataset, and its clinical performance was subsequently evaluated in an independent implementation cohort. Agreement with radiologist interpretations was assessed using Cohen’s kappa, and user acceptance was evaluated by 30 radiologists using the User Experience Questionnaire Plus (UEQ+). In the prospective implementation cohort, the model showed high multiclass classification performance, with an overall accuracy of 98.95%, a macro-average precision of 96.30%, and a macro-average recall of 96.29%. The model demonstrated high sensitivity for active tuberculosis (recall 97.97%) and high precision for post-TB sequelae (precision 91.67%), although recall for post-TB sequelae cases remained lower due to overlapping radiographic features. Agreement with radiologist interpretations was high, and user evaluation indicated very good perceived system quality, although hardware security remained the main area for improvement. The implemented deep learning system showed high agreement with radiologist interpretations and favorable user acceptance, suggesting its potential as a screening support tool for classifying normal, active TB, and post-TB sequelae on CXR. External validation across a diverse population is required in future work to establish generalizability.
D. Darmini, Bambang Budi Raharjo, M. Azam et al.· The Egyptian Journal of Radi...· 0 citations
Pneumonia remains one of the leading causes of respiratory illness worldwide and requires timely diagnosis to reduce
disease severity and mortality. Interpretation of chest X-ray images is a routine diagnostic procedure; however, manual
examination can be time-consuming and is influenced by the experience of radiologists. Computer-aided diagnostic systems
based on artificial intelligence have therefore attracted considerable attention as supportive tools for clinical decision-making.
This research presents a hybrid framework that combines deep feature extraction with traditional machine learning techniques
for automated pneumonia detection from chest X-ray images. Initially, chest X-ray images are preprocessed and analysed using
an EfficientNetV2 convolutional neural network to learn representative image features. Instead of directly performing end-toend classification, the extracted deep features are used to train three supervised machine learning classifiers: Random Forest,
Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). This hybrid strategy combines the feature
representation capability of deep learning with the classification efficiency of conventional machine learning algorithms. To
improve model transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to highlight image regions
that contribute most to the prediction. Furthermore, the selected classifier is integrated into a Streamlit-based web application
that enables users to upload chest X-ray images and obtain real-time diagnostic predictions.
Experimental evaluation demonstrates that the Support Vector Machine classifier achieved the highest performance among the
evaluated models, with an accuracy of 89.74%, precision of 86.22%, recall of 99.49%, F1-score of 92.38%, and ROC-AUC of
98.21%, indicating strong capability for distinguishing pneumonia from normal chest radiographs. The proposed framework
illustrates that combining deep feature extraction with machine learning classifiers can provide accurate, interpretable, and
computationally efficient pneumonia detection while supporting practical clinical deployment.
K. Veen, Pravitha R Prasad· International Journal for Re...· 0 citations
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