An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

By Okeke Stephen, Mangal Sain, Uchenna Joseph Maduh, Do-Un Jeong. Journal of Healthcare Engineering, 2019

537 citations recorded by the source corpus.

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f95bfe6f2ad14f3b1eaeea5e1e7e411ca59a2ad5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nour Eldeen M. Khalifa
2. Mohamed Loey
3. Florentin Smarandache
4. Philippe Smets

In Radiology, Nuclear Medicine and Imaging

1. Nour Eldeen M. Khalifa
2. Mohamed Loey
3. Florentin Smarandache
4. S. Mirjalili

Citations in the corpus

• COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches (2020, unknown): “[11] proposed a deep learning model to classify the pneumonia data from scratch to train the data.”
• Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning (2020, background): “[43] proposed a new study of classifying and detect the presence of pneumonia from a collection of chest X-ray image samples based on a ConvNet model trained from scratch based on dataset [44].”
• Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19 (2021, unknown): “[49] Non-COVID-19 VP (strain not specified) 5856 X-ray images The model achieved average Ac of 94.”