ItemArticle

NN-SVM: a hybrid neural networksupport vector machine framework for accurate pneumonia detection from chest X-rays

Title
NN-SVM: a hybrid neural networksupport vector machine framework for accurate pneumonia detection from chest X-rays
Creator
Jankatti, Santosh Kumar; Srinivasaiah, Raghavendra; Parveen, Mohammad Shahina; Kenchannavar, Harish H.; Sudha, Danthuluri; Narah, Srihari Sharma Karigiri; Shivaraj, Mahadev
Description
We present neural network (NN)-support vector machine (SVM), hybrid NN-SVM framework for three-class pneumonia detection (normal, bacterial, and viral) from chest X-rays (CXRs). Pretrained NN backbone is fine-tuned for radiographic textures; global average pooling (GAP) yields embeddings that feed calibrated radial basis function (RBF)-SVM. Standardized preprocessing (resize, normalization) and class-aware augmentation are applied. We report accuracy, precision, recall, F1-score, area under the curve (AUC), confusion matrices, and per-class receiver operating characteristic (ROC). Statistical significance is assessed via DeLong (AUC), McNemar (accuracy), and paired bootstrap (F1-score). Gradient-weighted class activation mapping (grad-CAM) supports interpretability; external validation and domain adaptation (batch normalization re-estimation and temperature scaling) assess robustness. NN-SVM attains 97.46% accuracy with strong macro-F1 and AUC. Compared with SoftMax head, SVM improves margin separation and calibration. We present NN-SVM, hybrid deep learning approach that combines transfer-learned convolutional neural networks (CNNs) with SVM classifier to automatically diagnose pneumonia from CXRs into three clinically relevant categories: viral pneumonia, bacterial pneumonia, and normal. We use pre-trained CNN to extract robust image embeddings after standardized preprocessing (resizing and normalization) and train RBF-kernel SVM on resulting features. Performance is evaluated with accuracy, precision, recall, F1-score, and confusion matrices. On labeled CXR dataset, NN-SVM achieves 97.46% accuracy, demonstrating strong diagnostic capability that can reduce radiologist burden and support timely clinical decision-making. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/
Source
IAES International Journal of Artificial Intelligence;Volume;15;Issue;2;pp.1349-1361
Date
01-01-2026
Publisher
Institute of Advanced Engineering and Science
Subject
Chest X-ray; CNN-SVM; Deep learning; Medical imaging; Pneumonia; Transfer learning
Coverage
Jankatti S.K., Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India; Srinivasaiah R., Department of Artificial Intelligence and Data Science Engineering, CHRIST University, Bengaluru, India; Parveen M.S., Department of Computer Science and Engineering (Data Science), Dayananda Sagar College of Engineering, Bengaluru, India; Kenchannavar H.H., Department of Computer Science and Engineering (Data Science), Dayananda Sagar College of Engineering, Bengaluru, India; Sudha D., Department of Computer Science and Engineering (Data Science), Dayananda Sagar College of Engineering, Bengaluru, India; Narah S.S.K., Dayananda Sagar University, Bengaluru, India; Shivaraj M., Department of Electronics and Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 20894872;
Format
online
Language
English
Type
Article

Citation

Jankatti, Santosh Kumar; Srinivasaiah, Raghavendra; Parveen, Mohammad Shahina; Kenchannavar, Harish H.; Sudha, Danthuluri; Narah, Srihari Sharma Karigiri; Shivaraj, Mahadev, “NN-SVM: a hybrid neural networksupport vector machine framework for accurate pneumonia detection from chest X-rays,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23081.

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