Summary of Predicting Lung Disease Severity Via Image-based Aqi Analysis Using Deep Learning Techniques, by Anvita Mahajan et al.
Predicting Lung Disease Severity via Image-Based AQI Analysis using Deep Learning Techniques
by Anvita Mahajan, Sayali Mate, Chinmayee Kulkarni, Suraj Sawant
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to predicting air quality and assessing lung disease severity using image data and neural networks is proposed in this study. The integrated method combines feature extraction from images using VGG16 and predictions of Air Quality Index (AQI) and lung disease severity using a neural network, Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN) algorithms. The approach achieves high testing accuracies of 87.44% for AQI and 97.5% for lung disease severity. This research has significant implications for air pollution forecasting and public health. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Air pollution is a big problem that makes people sick all around the world. There’s more data available now because of smart cities and sensors everywhere, which helps us predict air quality better. Scientists want to find a way to use image data and special computers to forecast air quality and figure out how bad lung disease is based on that information. They’re trying to make a new method that combines different techniques like VGG16 and neural networks to get even more accurate results. The goal is to be able to predict not just one type of pollutant, but many kinds at once, including things like PM10, O3, CO, SO2, NO2, and PM2.5. They want to compare their new method to other ways that people are doing it now to show how well it works. |
Keywords
» Artificial intelligence » Feature extraction » Neural network
