Review on Pneumonia Image Detection: A Machine Learning Approach

Abstract

This paper surveys and examines how computer-aided techniques can be deployed in detecting pneumonia. It also suggests a hybrid model that can effectively detect pneumonia while using the real-time medical image data in a privacy-preserving manner. This paper will explore how various preprocessing techniques such as X-rays can detect and classify multiple diseases. The survey also examines how different machine learning technologies like convolution neural network (CNN), k-nearest neighbor (KNN), RESNET, CheXNet, DECNET and artificial neural network (ANN) can be used in detecting pneumonia disease. In this article, we have performed a comprehensive review of the literature to find how we can combine hospitals and medical institutions to train the machine learning models from their datasets so that the ML algorithms can detect disease more efficiently and correctly. We have proposed the future work of using transfer learning combined with federated knowledge that could help the medical institutions and hospitals form a combined approach of performing medical image detection using real-time datasets. We have also explored the scope, future work and limitations of the proposed solution.

Novel Transfer Learning Approach for Early Detection of Childhood Pneumonia

“…These approaches maximise the computing power of computers to learn patterns from data such as images, text, audio, video, etc [9]. These techniques are constantly evolving and have been widely used in the medical field with positive results [32]. Deep learning techniques uses convolutional neural networks to learn from data.…”

“…In recent years, machine learning and deep learning algorithms have shown promise in medical image analysis, including the diagnosis of pneumonia [12]. These algorithms can automatically extract relevant features from medical images and classify them with high accuracy [39], [40].…”

“…These algorithms can automatically extract relevant features from medical images and classify them with high accuracy [39], [40]. Several studies have investigated the use of deep learning algorithms for the diagnosis of pneumonia in adults, but there is a paucity of research on the use of these algorithms for childhood pneumonia [12], [15], [18], [36]. Gajawelli et al [36] used a deep learning algorithm to classify CXR images of childhood pneumonia with high accuracy.…”