RadReader: An Enhanced AlexNet-Based GUI Application  for Pneumonia Prediction in Thoracic X-Ray Images

Giri Wahyu  Wiriasto; Ahdiat Aunul Hipzi; I Made Budi Suksmadana; Misbahuddin; Indira puteri Kinasih; Putu Aditya Wiguna

doi:10.29207/resti.v9i6.7023

Authors

Giri Wahyu Wiriasto Universitas Mataram
Ahdiat Aunul Hipzi Universitas Mataram
I Made Budi Suksmadana Universitas Mataram
Misbahuddin Universitas Mataram
Indira puteri Kinasih University of Brunei Darussalam
Putu Aditya Wiguna Universitas Mataram

DOI:

https://doi.org/10.29207/resti.v9i6.7023

Keywords:

Enhanced-alexNet (EAM), Pneumonia classification, Radiology application reader, Thoracic X-ray

Abstract

Recent advancements in radiology applications have led to user-friendly interfaces, improving pneumonia diagnosis by accurately differentiating between viral and bacterial pneumonia from thoracic X-rays. This approach enhances diagnostic precision and efficiency while offering intuitive real-time interaction for radiologists. This study aims to achieve two objectives: (i) developing a desktop-based radiology reader application, and (ii) modifying the alexNet architecture for classifying pneumonia based on thoracic X-ray datasets with the output encompassing pneumonia and normal cases. The desktop application assists radiologists in efficient image analysis and is developed using python–Tkinter. Integrate enhanced of AlexNet models which has been modified to better differentiate. The modified alexNet includes changes like adding max pooling in specific blocks and adjusting hidden layer neuron count. The dataset consists of 7442 images, with 4484 positive pneumonia and 2958 normal images obtained from the Mendeley websites. The enhanced alexNet (EAM) model achieves impressive results: 95.36% accuracy, 95.34% precision, 95.28% recall, and 95.31% F1-score for classifying bacterial pneumonia.

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