Abstract

Convolutional Neural Networks (CNNs) have become an effective tool for medical picture identification, and one of its most important applications is the detection of pneumonia. The study delves into the significance of trustworthy diagnostic techniques, examines several convolutional neural network (CNN) designs, and tackles obstacles such as data imbalance, noise, and uncertainty in medical imaging datasets. Techniques like Grad-CAM and LIME are also covered, as are assessment measures like accuracy, sensitivity, specificity, and AUC-ROC. Additionally, methods for studying decision-making processes are covered. The paper lays up a plan for further study into the use of CNNs for pneumonia diagnosis and emphasizes their potential. To make sure that CNN-based systems can be safely and effectively used in clinical practice, there has to be constant work to fix problems with data quality, model interpretability, and generalization.

Keywords: CNN; Pneumonia; Medical Imaging Analysis

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Citation

Shweta Saraswat., et al. “Enhancing Pneumonia Diagnosis with Convolutional Neural Networks: A Deep Learning Perspective". Clareus Scientific Medical Sciences 1.1 (2024): 09-18.

Copyright

2024 Shweta Saraswat., et al. Licensee Clareus Scientific Publications. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.