NEURAL NETWORK BASED REAL TIME PNEUMONIA DETECTION USING TRANSFER LEARNING AND IMAGE AUGMENTATION

Abstract

This paper aims to identify the detection of pneumonia disease using chest x-ray images by applying deep learning methods. Deep learning methods have tracked down their applications in different areas, and they are in effect broadly utilized in clinical medicines and diagnostics. To analyze viral/bacterial infections such as pneumonia, the assessment of chest X-ray images is frequently used, and the productivity of diagnosis can be altogether improved with the utilization of Computer-Aided Diagnostic (CAD) frameworks. Deep learning method such as Convolutional Neural Network (CNN) architecture is utilized in this paper for the characterization of chest X-ray images to analyze pneumonia. We have used the chest X-ray image dataset from Kaggle consisting of 4110 images. Image augmentations were performed on the dataset to oversample the dataset for the model to perform better. Then, at that point, we have built a custom CNN model and, also, we have utilized the transfer learning mechanism with CNN by using MobileNetV2 as the base model for the image classification problems. The average classification accuracy for our proposed CNN and MobileNetV2 based transfer learning method was 97%, and 97% for unbalanced and 97%, and 97%, for balanced datasets respectively. The satisfactory outcome of both models can significantly improve the accuracy and speed of pneumonia diagnosis. This would be very helpful in this pandemic situation in developing countries with limited resources and capabilities in the healthcare sector.