Manolescu, Vasile Denis and Buckley, N and Secco, Emanuele Lindo (2023) Convolutional Neural Network Applied to X-Ray Medical Imagery for Pneumonia Identification. In: 5th International Conference on Communication and Intelligent Systems (ICCIS 2023), 16-17 December, 2023, Jaipur, India. (Accepted for Publication)
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Abstract
Convolutional Neural Networks (CNN) are one of the most popular and effective approaches for image recognition. They have been widely used in various medical imaging applications, such as identifying tumors, detecting le-sions, and organ segmentation. The ability of CNNs to automatically learn and extract meaningful features from raw visual data makes them particularly well-suited for medical imaging tasks, where accuracy and reliability are critical. This paper presents an experimental study focused on analyzing the structure and functionality of convolutional neural networks by building an operational model capable of identifying cases of pneumonia from X-ray scans. The CNN model is trained, validated and tested on a dataset of over 5000 images, and the final re-sults show 99% precision and 98% accuracy, with a recall value of 98%.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Emanuele Secco |
| Date Deposited: | 07 Dec 2023 14:40 |
| Last Modified: | 31 Jan 2024 01:15 |
| URI: | https://hira.hope.ac.uk/id/eprint/4071 |
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