Application of Convolution Neural Network in disease detection such as pneumonia/covid detection through Chest X-ray/ heart beat classification etc.

Objective :- Application of Convolution Neural Network in disease detection such as pneumonia/covid detection through Chest X-ray/ heart beat classification etc.

Abstract : CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes image and video recognition, along with recommender systems and natural language processing (NLP).

A CNN uses a system much like a multilayer perceptron that has been designed for reduced processing requirements. The layers of a CNN consist of an input layer, an output layer and a hidden layer that includes multiple convolutional layers, pooling layers, fully connected layers and normalization layers. The removal of limitations and increase in efficiency for image processing results in a system that is far more effective, simpler to trains limited for image processing and natural language processing.

a).Application of Convolution Neural Network in pneumonia through Chest X-ray classification

Introduction:

Pneumonia is a lung parenchyma inflammation often caused by pathogenic microorganisms, factors of physical and chemical, immunologic injury and other pharmaceuticals. There are several popular pneumonia classification methods: (1) pneumonia is classified as infectious and non-infectious based on different pathogeneses in which infectious pneumonia is then classified to bacteria, virus, mycoplasmas, chlamydial pneumonia, and others, while non-infectious pneumonia is classified as immune-associated pneumonia, aspiration pneumonia caused by physical and chemical factors, and radiation pneumonia. (2) Pneumonia is classified as CAP (community-acquired pneumonia), HAP (hospital-acquired pneumonia) and VAP (ventilator-associated pneumonia) based on different infections, among which CAP accounts for a larger part. Because of the different range of pathogens, HAP is easier to develop resistance to various antibiotics, making treatment more difficult.

Related Work:

Several methods have been introduced to describe a brief process in pneumonia detection using chest X-ray images in recent years, especially some deep learning methods. Deep Learning has been successfully applied to improve the performance of computer aided diagnosis technology (CAD), especially in the field of medical imaging [5], image segmentation [6,7] and image reconstruction [8,9]. In 2017, Rampura et al. [10] proposed a classical deep learning network named DenseNet-121 [11], which was a 121-layer CNN model to accelerate the diagnosis for pneumonia

Background:

In the past few decades, machine learning (ML) algorithms have gradually attracted researchers’ attention. This type of algorithm could take full advantage of the giant computing power of calculators in images processing through given algorithms or specified steps.

However, traditional ML methods in classification tasks need to manually design algorithms or manually set feature extraction layers to classify images

Proposed CNN Model

Figure 4 illustrates the architecture of our proposed model that has been applied for the detection of whether the input image shows pneumonia. Figure 5 displays our model that contains a total of six layers, where we employed 3 × 3 kernel convolution layers whose strides are 1 × 1 and the activation function is ReLU. After each convolution layer, a 2 × 2 strides kernel operation was employed as a max-pooling operation to retain the maximum of each sub-region, which is split according to strides. Besides, we set several drop layers to randomly fit weights to zero, aiming to improve the model performance. Then two densely fully-connected layers followed by Sigmoid function are utilized to take full advantage of the features extracted through previous layers, outputting the possibility of patients suffering from pneumonia or not. As illustrated above, the input channel is 224 × 224 × 1 and the output size is y ∈ {0, 1}, where 0 denotes that the image does not show pneumonia, while 1 denotes that the image shows pneumonia

      b).Application of Convolution Neural Network in covid detection through heart beat  classification: -

Introduction:-

CNN is used in pattern recognition with superior feature learning capabilities, being a suitable model to deal with image data. Indeed, CNN is a dominant architecture of DL for image classification and can rival human accuracies in many tasks. CNN uses hierarchical layers of tiled convolutional filters to mimic the effects of human receptive fields on feedforward processing in the early visual cortex thereby exploiting the local spatial correlations present in images while developing robustness to natural transformations such as changes of viewpoint

or scale. A CNN-based model generally requires a large set of training samples to achieve good generalization capabilities. Its basic structure is represented as a sequence of Convolutional—Pooling—Fully Connected Layers possibly with other intermediary layers for normalization and/or dropout.

Network architecture:-

1. Input layer

The input layer basically depends on the dimension of the images. In our network, all images must have the same dimension presented as a grayscale (single colour channel) image.

1. Batch Normalization layer.

Batch normalization converts the distribution of the inputs to a standard normal distribution with mean 0 and variance 1, avoiding the problem of gradient dispersion and accelerating the training process.

1. Convolutional layer.

Convolutions are the main building blocks of a CNN. Filter kernels are slid over the image and for each position the dot product of the filter kernel and the part of the image covered by the kernel is taken. All kernels used in this layer are 3 × 3 pixels. The chosen activation function of convolutional layers is the rectified linear unit (ReLU), which is easy to train due to its piecewise linear and sparse characteristics.

1. Max pooling layer.

Max pooling is a sub-sampling procedure that uses the maximum value of a window as the output. The size of such a window was chosen as 2 × 2 pixels.

1. Fire layer.

A fire module is comprised of a squeeze convolutional layer (which has only 1 × 1 filters) feeding into an expand layer that has a mix of 1 × 1 and 3 × 3 convolution filters. The use of a fire layer could reduce training time while still extracting data characteristics in comparison with dense layers with the same number of parameters. The layer is represented in Fig 4 in which Input and Output have the same dimensions.

Proposed model:-

Despite their self-learning capacity and superior prediction performance, LWL and SOM models achieve human-like precision in image description and prediction issues. Our framework aims mainly at providing distinguishing visual properties and a quick diagnostic system that can be used to classify new COVID-19 X-rays. This technique can also be useful to clinicians as a treatment plan that can be used depending on the type of infection and can provide prompt decisions.

Related Work:-

Real-time reverse transcription-polymerase chain reaction (RT-PCR) is the primary research technique currently in use for COVID-19 diagnosis. Chest radiographic images, such as CT images and X-rays, are critical for the early diagnosis and treatment of the condition. The low sensitivity of RT-PCR (60–70%) allows symptoms to be detected by analysing radiographic images of patients, even though adverse findings are obtained.

Conclusion:-

Within this context, the literature suggests that the diagnosis may be assisted by the use of data mining methods to classify pneumonia disease in chest X-rays. However, the issue is much more difficult when we look at chest images of patients suffering from pneumonia caused by multiple types of pathogens and attempt to forecast a particular form of pneumonia (COVID-19).