Pneumonia Detection using machine learning - with PyTorch

Last update: Jul 7, 2022

Overview

Pneumonia Detection

Pneumonia Detection using machine learning.

Training was done in colab:

DEMO:

Result (Confusion Matrix):

Data

I uploaded my dataset to kaggle I used a modified version of this dataset from kaggle. Instead of NORMAL and PNEUMONIA I split the PNEUMONIA dataset to BACTERIAL PNUEMONIA and VIRAL PNEUMONIA. This way the data is more evenly distributed and I can distinguish between viral and bacterial pneumonia. I also combined the validation dataset with the test dataset because the validation dataset only had 8 images per class.

This is the resulting distribution:

Processing and Augmentation

I resized the images to 150x150 and because some images already were grayscale I also transformed all the images to grayscale.

Additionaly I applied the following transformations/augmentations on the training data:

transforms.Resize((150, 150)),
transforms.Grayscale(),
transforms.ToTensor(),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomRotation(45)

and those transformations on the test data:

transforms.Resize((150, 150)),
transforms.Grayscale(),
transforms.ToTensor(),

This is the resulting data:

I also used one-hot encoding for the labels!

Model

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 16, 148, 148]             160
              ReLU-2         [-1, 16, 148, 148]               0
       BatchNorm2d-3         [-1, 16, 148, 148]              32
            Conv2d-4         [-1, 16, 146, 146]           2,320
              ReLU-5         [-1, 16, 146, 146]               0
       BatchNorm2d-6         [-1, 16, 146, 146]              32
         MaxPool2d-7           [-1, 16, 73, 73]               0
            Conv2d-8           [-1, 32, 71, 71]           4,640
              ReLU-9           [-1, 32, 71, 71]               0
      BatchNorm2d-10           [-1, 32, 71, 71]              64
           Conv2d-11           [-1, 32, 69, 69]           9,248
             ReLU-12           [-1, 32, 69, 69]               0
      BatchNorm2d-13           [-1, 32, 69, 69]              64
        MaxPool2d-14           [-1, 32, 34, 34]               0
           Conv2d-15           [-1, 64, 32, 32]          18,496
             ReLU-16           [-1, 64, 32, 32]               0
      BatchNorm2d-17           [-1, 64, 32, 32]             128
           Conv2d-18           [-1, 64, 30, 30]          36,928
             ReLU-19           [-1, 64, 30, 30]               0
      BatchNorm2d-20           [-1, 64, 30, 30]             128
        MaxPool2d-21           [-1, 64, 15, 15]               0
           Conv2d-22          [-1, 128, 13, 13]          73,856
             ReLU-23          [-1, 128, 13, 13]               0
      BatchNorm2d-24          [-1, 128, 13, 13]             256
           Conv2d-25          [-1, 128, 11, 11]         147,584
             ReLU-26          [-1, 128, 11, 11]               0
      BatchNorm2d-27          [-1, 128, 11, 11]             256
        MaxPool2d-28            [-1, 128, 5, 5]               0
          Flatten-29                 [-1, 3200]               0
           Linear-30                 [-1, 4096]      13,111,296
             ReLU-31                 [-1, 4096]               0
          Dropout-32                 [-1, 4096]               0
           Linear-33                 [-1, 4096]      16,781,312
             ReLU-34                 [-1, 4096]               0
          Dropout-35                 [-1, 4096]               0
           Linear-36                    [-1, 3]          12,291
          Softmax-37                    [-1, 3]               0
================================================================
Total params: 30,199,091
Trainable params: 30,199,091
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.09
Forward/backward pass size (MB): 27.95
Params size (MB): 115.20
Estimated Total Size (MB): 143.24
----------------------------------------------------------------

Visualization using Streamlit

The webapp is not hosted because the model is too large. I'd have to host it on a server. This is just to visualize.

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

1 Jan 1, 2022

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and build their own methods.

405 Jan 4, 2023

Pose Detection and Machine Learning for real-time body posture analysis during exercise to provide audiovisual feedback on improvement of form.

Posture: Pose Tracking and Machine Learning for prescribing corrective suggestions to improve posture and form while exercising. This repository conta

10 Nov 11, 2022

This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Skin Lesion detection using YOLO This project deal

1 Nov 22, 2021

Human Detection - Pedestrian Detection using OpenCV Python

Pedestrian Detection using OpenCV Python Follow us on Instagram for Machine Lear

1 Jan 23, 2022

Pneumonia Detection using machine learning - with PyTorch

Related tags

Overview

Pneumonia Detection

Data

Processing and Augmentation

Model

Visualization using Streamlit

You might also like...

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

Pose Detection and Machine Learning for real-time body posture analysis during exercise to provide audiovisual feedback on improvement of form.

Final project for machine learning (CSC 590). Detection of hepatitis C and progression through blood samples.

End-to-end machine learning project for rices detection

Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.

Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite.

This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.

Human Detection - Pedestrian Detection using OpenCV Python

Owner

Wilhelm Berghammer

CasualHealthcare's Pneumonia detection with Artificial Intelligence (Convolutional Neural Network)

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Intrusion Detection System using ensemble learning (machine learning)

MOT-Tracking-by-Detection-Pipeline - For Tracking-by-Detection format MOT (Multi Object Tracking), is it a framework that separates Detection and Tracking processes?

This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!