Pneumonia Disease Detection in X-Ray Images Using A Deep Learning Approach with CNN and Alexnet Architecture

Anissa Putri A. Karim, Abdul Mubarak, Hairil Kurniadi Sirajudin

Abstract


Effective treatment of pneumonia, a respiratory illness, depends on a prompt and precise diagnosis. However, using medical investigations to diagnose pneumonia can be laborious and rely on the skill of radiologists. In order to automatically identify pneumonia from chest X-ray pictures, this study intends to create an artificial intelligence model utilizing a Convolutional Neural Network (CNN) with the AlexNet architecture. 2,806 X-ray pictures that were classified as either normal or pneumonia were used. A variety of preprocessing methods were used to improve the quality of the data, and AlexNet, which had previously been trained on ImageNet, was used for transfer learning to increase the accuracy of detection. The model's accuracy, precision, and recall were 95.44%, 99%, and 94%, respectively. However, because Google Colab uses temporary sessions, speed varies a little when it is rerun. In spite of this, the model continuously maintains an accuracy of over 90%. Furthermore, users can upload X-ray photos and get immediate results using a Gradio-based interface, which makes it accessible to those without technical knowledge. This study lays the groundwork for using AI to the diagnosis of pneumonia with the goal of increasing the effectiveness and speed of medical imaging analysis.


Keywords


Pneumonia; Convolutional Neural Network; AlexNet; X-Ray Image; Deep Learning.

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References


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DOI: https://doi.org/10.17509/coelite.v4i1.80881

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