Pneumonia detection from X-ray images using Convolutional neural networks and python

Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure.

Project Title

Pneumonia detection from X-ray images using Convolutional neural networks and python

Project Area of Specialization

Artificial Intelligence

Project Summary

Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121

Project Objectives

This AI project is beneficial for the healthcare domain to detect diseases like pneumonia, heart ailments, etc., and provide medical consultation to the patients. The software solution will be trained with massive details on pneumonia. When users share their health-related issues and symptoms, the software can process the information and check it against its database for possibilities related to those details. It can use data mining to provide the most precise disease corresponding to the patient’s details.

Project Implementation Method

In this study, we designed an ensemble framework of three classifiers , GoogLeNet [34], ResNet-18 [35], and DenseNet-121 [36], using a weighted average ensemble scheme wherein the weights allocated to the classifiers are generated using a novel scheme

The GoogLeNet architecture proposed by Szegedy et al is a 22-layer deep network consisting of “inception modules,” instead of uniformly progressive layers. An inception block accommodates a large number of units at each stage by hosting parallel convolution and pooling layers, resulting in an uncontrolled computational complexity because of the increased number of parameters. To control the computational complexity, the GoogLeNet model uses inception blocks with dimension reduction

The ResNet-18 model proposed by He et al is based on a residual learning framework, which increases the efficiency of deep network training. The residual blocks in the ResNet models facilitate the optimization of the overall network, which in turn improves model accuracy

The DenseNet architectures proposed by Huang et al provide a rich feature representation while being computationally efficient. The primary reason is that, in each layer of the DenseNet model, the feature maps in the current layer are concatenated with those from all the preceding layers

The ensemble learning model helps incorporate the discriminative information of all its constituent models, and thus, its predictions are superior to those of any of its constituent base learners. Weighted average ensembling is a powerful classifier fusion mechanism

Benefits of the Project

The software solution will be trained with massive details on pneumonia or other diseases. When users share their health-related issues and symptoms, the software can process the information and check it against its database for possibilities related to those details. It can use data mining to provide the most precise disease corresponding to the patient’s details. This way, a patient’s disease can be detected, and they can get proper treatment. And to design the software, you must determine the most efficient CNN model analytically and comparatively to achieve pneumonia detection from X-ray images using feature extraction.

Technical Details of Final Deliverable

In this study, we designed an ensemble framework of three classifiers , GoogLeNet [34], ResNet-18 [35], and DenseNet-121 [36], using a weighted average ensemble scheme wherein the weights allocated to the classifiers are generated using a novel scheme

The GoogLeNet architecture proposed by Szegedy et al is a 22-layer deep network consisting of “inception modules,” instead of uniformly progressive layers. An inception block accommodates a large number of units at each stage by hosting parallel convolution and pooling layers, resulting in an uncontrolled computational complexity because of the increased number of parameters. To control the computational complexity, the GoogLeNet model uses inception blocks with dimension reduction

The ResNet-18 model proposed by He et al is based on a residual learning framework, which increases the efficiency of deep network training. The residual blocks in the ResNet models facilitate the optimization of the overall network, which in turn improves model accuracy

The DenseNet architectures proposed by Huang et al provide a rich feature representation while being computationally efficient. The primary reason is that, in each layer of the DenseNet model, the feature maps in the current layer are concatenated with those from all the preceding layers

The ensemble learning model helps incorporate the discriminative information of all its constituent models, and thus, its predictions are superior to those of any of its constituent base learners. Weighted average ensembling is a powerful classifier fusion mechanism

Final Deliverable of the Project

Software System

Core Industry

Health

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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