Pneumonia detection
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Stacked Generalization-Based Deep Learning Approach for Pneumonia Detection
Abstract: The proposed work focuses on a stacked generalization-based approach for diagnosing pneumonia from chest X-ray images. It utilizes regularization, early stopping, and data augmentation to deal with overfitting. It uses safe level SMOTE to deal with class imbalance and attention-based feature fusion to adaptively weigh features based on their importance. It uses two publicly available datasets (RSNA and Kermany) with ground truth provided by expert radiologists. The proposed work used …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 20–31 Read article
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Advancements in Pneumonia X-Ray Image Detection: A Review
Abstract: Pneumonia remains a primary cause of morbidness and mortality worldwide, necessitating the continuous advancement of diagnostic techniques for timely and accurate detection. Pneumonia is common, it is potentially a life-threatening infection for respiration, poses significant challenges to healthcare systems worldwide. Recently, the arrival of deep learning techniques has stirred up the field of medical imaging, offering promising avenues for enhanced pneumonia detection. In this paper, the advancements in pneumonia detection …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 1, 2025 · pp. 1–11 Read article
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Pneumonia Detection and Classification Using Deep Learning
Abstract: Pneumonia, an infectious lung disease primarily caused by bacteria, often exacerbated by environmental factors, leads to the accumulation of pus in the lung’s alveoli. Accurate diagnosis through chest X-rays, ultrasounds, or lung biopsies is crucial to avoid misdiagnosis and ensure proper treatment, crucial for patients’ quality of life. Diagnostic capacities have been greatly improved by deep learning advances, especially with convolutional neural networks (CNNs). This research presents a robust CNN-based …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 9–19 Read article