ComputingComputer ScienceHealth InformaticsArtificial IntelligenceComputer VisionMachine LearningChest RadiographyChest X-ray InterpretationDeep LearningMedical Image AnalysisMedical ImagingNeural NetworksComputer-aided Chest X-ray DiagnosisConvolutional Neural Network DetectionConvolutional Neural NetworksDeep Learning-based DetectionDeep Learning-based DiagnosisModel Accuracy AssessmentMultilayer PerceptronPerceptron
It is observed that pneumonia is the largest cause of death in toddlers. Early detection would facilitate in early treatment, hence, saving many lives. This paper intend to develop a method for automating the detection of pneumonia using chest X-rays and comparing convolutional neural network and multilevel perceptron. Custom Convolutional neural network and multi-layer perceptron were implemented on a chest X-ray dataset by Kaggle. A GUI was created that accepts the chest X-ray and predicts the presence of pneumonia and gives the congestion percentage. The model accuracy was 92.63% and 77.56% for convolutional neural network and multilayer perceptron respectively. The GUI developed gives favourable outcomes. Convolutional neural network showed better results as compared to multi-layer perceptron. Thus, the custom convolutional neural network was used for the GUI.
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Can AI Help in Screening Viral and COVID-19 Pneumonia?
Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar et al. · IEEE Access · 2020 · 1.9K citations · Full text
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep\n Learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu et al. · arXiv (Cornell University) · 2017 · 1.5K citations · Full text