Description:Now that we've shown that the use case diagram is dynamic, there should be some internal or external factors that influence how it interacts. Actors refer to both internal and external agents. Actors, use cases, and their relationships make up use case diagrams.The diagram is used to represent an application's system or subsystem. A single use case diagram depicts a system's specific capabilities. As a result, a large number of use case diagrams are utilised to model the overall system. At its most basic level, a use case diagram depicts the parameters of a use case and represents a user's interaction with the system. A use case diagram can depict the many sorts of users of a system and the case, and it is frequently complemented by other diagrams. , Claims:CLAIM:
We Claim
1. A method for classifying wheather the person is suffering from pneumonia or not can be detected and it comprises Numpy and tensorflow packages from Python i.e., Spyder.
2. The method of claim 1 wherein data is retrieved from the Pneumonia dataset.
3. The method of claim 1 further comprising preliminary analysis from Pneumonia dataset by preprocessing it using standardization method.
4. The method of claim 1 where in Convolutional Neural Network(CNN) Algorithm which is a deep learning Algorithm is used to train and classify the data from the dataset.CNN Algorithm has 3 layers:
a. Convolutional Layer: This layer is the first layer that is used to extract the various features from the input images. In this layer, the mathematical operation of convolution is performed between the input image and a filter of a particular size MxM. By sliding the filter over the input image, the dot product is taken between the filter and the parts of the input image with respect to the size of the filter (MxM).The output is termed as the Feature map which gives us information about the image such as the corners and edges. Later, this feature map is fed to other layers to learn several other features of the input image.
b. Pooling Layer: In most cases, a Convolutional Layer is followed by a Pooling Layer. The primary aim of this layer is to decrease the size of the convolved feature map to reduce the computational costs. This is performed by decreasing the connections between layers and independently operates on each feature map. Depending upon method used, there are several types of Pooling operations. In Max Pooling, the largest element is taken from feature map. Average Pooling calculates the average of the elements in a predefined sized Image section.The total sum of the elements in the predefined section is computed in Sum Pooling. The Pooling Layer usually serves as a bridge between the convolutional Layer and the FC Layer
c. Fully Connected Layer: The Fully Connected (FC) layer consists of the weights and biases along with the neurons and is used to connect the neurons between two different layers. These layers are usually placed before the output layer and form the last few layers of a CNN Architecture. In this, the input image from the previous layers are flattened and fed to the FC layer.The flattened vector then undergoes few more FC layers where the mathematical functions operations usually take place. In this stage, the classification process begins to take place.
5. The method of claim 1 where this is a user-friendly process and has highest accuracy.