2. India accounts for 20 percent of the death worldwide caused by
pneumonia
Pneumonia affects the lungs and causes about 18% of all deaths in
children under five years old.
Chest X-rays are the most common and easiest way to detect
pneumonia.
X-ray images are often unclear and can be confused with other
diseases that have similar features.
Problem
Statement
3. To reduce the human eye error in diagnosing the disease.
To develop a deep learning framework to automatically diagnose
pneumonia using chest X-ray images.
To classify the result as normal cases or pneumonia cases.
Objective
4. Machine Learning library ,sklearn used with many subordinate
models such as model classifiers, and metrics etc.
Deep Learning library: Keras, Pytorch etc..
Google Colab used for implementation.
Data set available on open source platforms
Programming Language: Python
TECHNIQUES/
TECHNOLOGIES
USED
5. The dataset is taken from kaggle.
The dataset is organized into 2 folders (train, test) and contains
subfolders for each image category (Pneumonia/Normal).
Dataset
7. Preprocessing
Preprocessing
Preprocessing is the removal of systematic noise from the data
Image generator object which performs random rotations,
shifts, flips, crops, and sheers on the image dataset.
Enhancement
• Image enhancement is the process of adjusting digital images so
that the results are more suitable for display or further image
analysis.
• Enhancement remove noise, sharpen, or brighten an image,
making it easier to identify key features.
8. Preprocessing
NOISE REMOVAL:
• Digital images are prone to various types of noise.
Noise is the result of errors in the image acquisition process that
result in pixel values that do not reflect the true intensities of the real
scene
There are several ways that noise can be introduced into an image,
depending on how the image is created. Electronic transmission of
image data can introduce noise.
9. SEGMENTATION:
• Image segmentation is the process of dividing an image
into multiple parts.
• Simplify and easier to analyze.
• This is typically used to identify objects or other
relevant information in digital images.
segmentation:
10. CNN has two parts Feature extraction and classification
A Convolutional Neural Network (CNN) is a deep learning
algorithm that can recognize and classify features in images for
computer vision.
A convolution tool that splits the various features of the image for
analysis
Feature extraction - several convolution layers followed by max-
pooling and an activation function.
The classifier -fully connected layers.
Overview of -
CNN
18. VGG-16
VGG stands for Visual Geometry Group
TheVGG architecture consists of blocks, where each block is
composed of 2D Convolution and Max Pooling layers.
There are total of 13 convolutional layers and 3 fully connected
layers inVGG16 architecture.
VGG-16 is a convolutional neural network that is 16 layers deep.
The network has an image input size of 224-by-224.
24. Mobile net
MobileNetV2 is a convolutional neural network architecture that
seeks to perform well on mobile devices.
It is based on an inverted residual structure where the residual
connections are between the bottleneck layers
The architecture of MobileNetV2 contains the initial fully
convolution layer with 32 filters, followed by 19 residual
bottleneck layers.
...
30. INFERENCE
•CNN model for Pneumonia disease detection –implemented.
•Mobile Net model Pneumonia Pneumonia Disease detection –
implemented.
VGG-16 model for Pneumonia disease detection –implemented
Classification of all models –tested.
VGG-16 outperformed Mobile Net model, CNN Model for
pneumonia disease detection in Chest X-rays.
31. CONCLUSION
•Pneumonia identified using Deep learning models.
•Pneumonia Disease was classified using Deep learning models.
•CNN model ,VGG-16 and Mobile net was used and compared for
accuracy and performance.
•VGG-16 model proved to produce accuracy than CNN Model and
Mobile net for detecting Pneumonia disease in Chest X-rays.
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