https://colab.research.google.com/drive/1NOe-oLRgqiLVA4xWe97Ijh78-EASrud2?usp=sharing
The increasing frequency and magnitude of viral outbreaks in recent decades, by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive viral diagnostic methods. The symptoms of the disease can vary and include dyspnoea, high fever, runny nose, and cough according to WHO. Instead of waiting for blood testing or RT-pcr which takes approx. 5-6 hours to generate result. These cases can most commonly be diagnosed using chest X-ray imaging analysis
Deep learning has shown a dramatic increase in the medical applications in general and specifically in medical image-based diagnosis. Due to the very promising results provided by CNNs in medical image analysis and classification, they are inspired by the visual system of the human brain. The idea behind the CNNs is to make computers capable of viewing the world as humans view it. This way CNNs can be used in the fields of image recognition and analysis, image classification, and natural language processing.
Convolutional neural networks are distinguished from other neural networks by their superior performance with image, speech, or audio signal inputs. They have three main types of layers, which are: • Convolutional layer • Pooling layer • Fully-connected (FC) layer
The convolutional layer is the core building block of a CNN, and it is where the majority of computation occurs. It requires a few components, which are input data, a filter, and a feature map. Let’s assume that the input will be a colour image, which is made up of a matrix of pixels in 3D. This means that the input will have three dimensions—a height, width, and depth—which correspond to RGB in an image. We also have a feature detector, also known as a kernel or a filter, which will move across the receptive fields of the image, checking if the feature is present. This process is known as a convolution.
Pooling layers, also known as down sampling, conducts dimensionality reduction, reducing the number of parameters in the input.While a lot of information is lost in the pooling layer, it also has a number of benefits to the CNN. They help to reduce complexity, improve efficiency, and limit risk of overfitting.
The name of the full-connected layer aptly describes itself. As mentioned earlier, the pixel values of the input image are not directly connected to the output layer in partially connected layers. However, in the fully-connected layer, each node in the output layer connects directly to a node in the previous layer.