ArticleSensors (Basel, Switzerland)2024
Concatenated CNN-Based Pneumonia Detection Using a Fuzzy-Enhanced Dataset.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Limitations of Retrospective Machine Learning Models for Predicting Tracheostomy After Cardiac Surgery.Diagnostics (Basel, Switzerland) · 2026Article
- Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method.Sensors (Basel, Switzerland) · 2025Article
- Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram Equalization Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection.Sensors (Basel, Switzerland) · 2025Article
- Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line classifiers and datasets with model optimization and interpretability.PeerJ. Computer science · 2025Article
- Diagnosis of pneumonia from chest X-ray images using YOLO deep learning.Frontiers in neurorobotics · 2025Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Pneumonia is a form of acute respiratory infection affecting the lungs. Symptoms of viral and bacterial pneumonia are similar. Rapid diagnosis of the disease is difficult, since polymerase chain reaction-based methods, which have the greatest reliability, provide results in a few hours, while ensuring high requirements for compliance with the analysis technology and professionalism of the personnel. This study proposed a Concatenated CNN model for pneumonia detection combined with a fuzzy logic-based image improvement method. The fuzzy logic-based image enhancement process is based on a new fuzzification refinement algorithm, with significantly improved image quality and feature extraction for the CCNN model. Four datasets, original and upgraded images utilizing fuzzy entropy, standard deviation, and histogram equalization, were utilized to train the algorithm. The CCNN's performance was demonstrated to be significantly improved by the upgraded datasets, with the fuzzy entropy-added dataset producing the best results. The suggested CCNN attained remarkable classification metrics, including 98.9% accuracy, 99.3% precision, 99.8% F1-score, and 99.6% recall. Experimental comparisons showed that the fuzzy logic-based enhancement worked significantly better than traditional image enhancement methods, resulting in higher diagnostic precision. This study demonstrates how well deep learning models and sophisticated image enhancement techniques work together to analyze medical images.
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