Jul 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 1672-1681· 0 citations
TL;DR
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 and illustrates that combining deep feature extraction with machine learning classifiers can provide accurate, interpretable, and computationally efficient pneumonia detection while supporting practical clinical deployment.
Abstract
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.
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
Pneumonia is a critical respiratory illness that remains a significant source of morbidity and mortality worldwide. This again stresses the need for effective and efficient diagnostic support systems.” Chest X-ray imaging is an integral part of pneumonia diagnosis. Manual interpretation of X-ray images is a time-consuming process and may vary from person to person. This study proposes an approach for the automatic detection of pneumonia using deep learning techniques and chest X-ray images. A publicly available dataset consisting of 5,863 chest X-ray images is used for the study. Preprocessing techniques such as resizing, normalisation, and data augmentation are employed on the images to enhance the generalisation capability of the model. Two state-of-the-art architectures of convolutional neural networks, DenseNet121 and EfficientNet-B0, are used for the study. EfficientNet-B0 was shown to perform better in classification than DenseNet121 based on accuracy and reliability after the models were tested and assessed. Gradient-weighted Class Activation Map (Grad-CAM) was utilized to emphasize the pertinent regions of the chest X-ray images that the model concentrated on for the classification in order to enhance the interpretability of the suggested model. The findings of the experiment demonstrated that pneumonia may be detected using the suggested explainable deep learning framework.
C. Sivamani, Joselyn Immaculate, Sunfiya J et al.· International Conference Com...· 0 citations
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.
Zoya Nasreen, Afshan Fatima, Ruqiya Fatima· International Journal of AI...· 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
Background: Pulmonary tuberculosis (PTB) remains one of the leading infectious causes of mortality worldwide, and delayed or missed diagnosis continues to hinder tuberculosis (TB) elimination programs, particularly in resource-constrained settings. Chest X-ray (CXR) interpretation and routine laboratory investigations are widely accessible, yet unimodal diagnostic approaches individually exhibit limited sensitivity and specificity.
Objective: This study proposes and evaluates an artificial intelligence (AI)-based multimodal framework that fuses deep convolutional features extracted from CXR images with routine hematological and inflammatory laboratory markers to improve automated PTB detection.
Methods: A retrospective dataset of 5,200 patients (2,540 TB-positive, 2,660 TB-negative), each comprising a digital CXR image and eight routine laboratory parameters (complete blood count indices, erythrocyte sedimentation rate, and C-reactive protein), was used. A ResNet-50 convolutional neural network (CNN) extracted 2048-dimensional image embeddings, while an Extreme Gradient Boosting (XGBoost) encoder generated 64-dimensional clinical embeddings from laboratory data. These were combined through an attention-weighted fusion layer feeding a fully connected classification head. Performance was benchmarked against four unimodal baselines (support vector machine, random forest, XGBoost, and CNN-only) using accuracy, sensitivity, specificity, precision, F1-score, and the area under the receiver operating characteristic curve (AUC).
Results: The proposed multimodal fusion model achieved a test-set accuracy of 96.2%, sensitivity of 95.8%, specificity of 96.6%, F1-score of 96.0%, and AUC of 0.984, outperforming the best unimodal baseline (CNN-only, 89.6% accuracy, AUC = 0.932) by a statistically significant margin (p < 0.001, McNemar's test). Erythrocyte sedimentation rate and C-reactive protein emerged as the most influential laboratory predictors.
Conclusion: Fusing radiological and hematological information through a multimodal deep learning pipeline substantially improves automated PTB screening performance relative to single-modality models and may serve as a low-cost, scalable triage tool in high-burden settings, pending prospective multi-center validation.
Ankita, Suvigya, Sanket Shinde et al.· International Journal For Mu...· 0 citations
In the medical field, automated medical image analysis is a key application of deep learning that aids in fast and uniform diagnosis of a disease. In this study, we propose DenseNet121-Attention framework for three class chest X-ray classification: Normal, Bacterial Pneumonia and Viral Pneumonia. The method proposed involves image preprocessing, data augmentation, transfer learning-based deep feature extraction, attention-driven feature refinement, Softmax classification and Grad-CAM based interpretability. DenseNet121 is used to extract hierarchy of radiological features and the attention mechanism is used to focus on the pulmonary areas of importance and to ignore less relevant information from the image. The overall accuracy, precision, sensitivity, F1-score and macro-AUC achieved are 84.29%, 84%, 83%, 83% and 94.54%, respectively. The class-wise analysis reveals excellent detection of bacterial pneumonia (94% sensitivity), high precision of the Normal class (93%) and high AUC score (96.10%). Results showed that the proposed framework is effective in efficiently and interpretively classifying the chest X-rays and can be used to support computer-aided diagnosis (CAD). Future directions will involve working with more multicenter datasets, multimodal imaging approaches, working with a more transformer-like model, and enhanced explainable AI methods.
D. Barbole· Natural Resources for Human...· 0 citations
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