Pneumonia Detection in Chest X-Rays using Neural Networks
- URL: http://arxiv.org/abs/2204.03618v1
- Date: Thu, 7 Apr 2022 17:39:12 GMT
- Title: Pneumonia Detection in Chest X-Rays using Neural Networks
- Authors: Narayana Darapaneni, Ashish Ranjan, Dany Bright, Devendra Trivedi,
Ketul Kumar, Vivek Kumar, and Anwesh Reddy Paduri
- Abstract summary: We have proposed the CNN model for the classification of Chest X-ray images for Radiological Society of North America Pneumonia datasets.
The proposed method is based on a non-complex CNN and the use of transfer learning algorithms like Xception, InceptionV3/V4, EfficientNetB7.
- Score: 0.6639684533393106
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: With the advancement in AI, deep learning techniques are widely used to
design robust classification models in several areas such as medical diagnosis
tasks in which it achieves good performance. In this paper, we have proposed
the CNN model (Convolutional Neural Network) for the classification of Chest
X-ray images for Radiological Society of North America Pneumonia (RSNA)
datasets. The study also tries to achieve the same RSNA benchmark results using
the limited computational resources by trying out various approaches to the
methodologies that have been implemented in recent years. The proposed method
is based on a non-complex CNN and the use of transfer learning algorithms like
Xception, InceptionV3/V4, EfficientNetB7. Along with this, the study also tries
to achieve the same RSNA benchmark results using the limited computational
resources by trying out various approaches to the methodologies that have been
implemented in recent years. The RSNA benchmark MAP score is 0.25, but using
the Mask RCNN model on a stratified sample of 3017 along with image
augmentation gave a MAP score of 0.15. Meanwhile, the YoloV3 without any
hyperparameter tuning gave the MAP score of 0.32 but still, the loss keeps
decreasing. Running the model for a greater number of iterations can give
better results.
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