Pneumonia Detection using machine learning - with PyTorch

Overview

Pneumonia Detection

Pneumonia Detection using machine learning.

Training was done in colab:

Training In Colab


DEMO:

gif

Result (Confusion Matrix):

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:

data 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:

sample images

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.

Owner
Wilhelm Berghammer
Artificial Intelligence Student @ JKU (1st year)
Wilhelm Berghammer
Python periodic table module

elemenpy Hello! elements.py is a small Python periodic table module that is used for calling certain information about an element. Installation Instal

Eric Cheng 2 Dec 27, 2021
Epidemiology analysis package

zEpid zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The purpose of this library is

Paul Zivich 111 Jan 08, 2023
Img-process-manual - Utilize Python Numpy and Matplotlib to realize OpenCV baisc image processing function

Img-process-manual - Opencv Library basic graphic processing algorithm coding reproduction based on Numpy and Matplotlib library

Jack_Shaw 2 Dec 12, 2022
Differentiable Surface Triangulation

Differentiable Surface Triangulation This is our implementation of the paper Differentiable Surface Triangulation that enables optimization for any pe

61 Dec 07, 2022
Node-level Graph Regression with Deep Gaussian Process Models

Node-level Graph Regression with Deep Gaussian Process Models Prerequests our implementation is mainly based on tensorflow 1.x and gpflow 1.x: python

1 Jan 16, 2022
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

SimMIM By Zhenda Xie*, Zheng Zhang*, Yue Cao*, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai and Han Hu*. This repo is the official implementation of

Microsoft 674 Dec 26, 2022
CondenseNet V2: Sparse Feature Reactivation for Deep Networks

CondenseNetV2 This repository is the official Pytorch implementation for "CondenseNet V2: Sparse Feature Reactivation for Deep Networks" paper by Le Y

Haojun Jiang 74 Dec 12, 2022
PyBrain - Another Python Machine Learning Library.

PyBrain -- the Python Machine Learning Library =============================================== INSTALLATION ------------ Quick answer: make sure you

2.8k Dec 31, 2022
(AAAI2020)Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing This repository contains pytorch source code for AAAI2020 oral paper: Grapy-ML

54 Aug 04, 2022
An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.

简介 通过PaddlePaddle框架复现了论文 Real-time Convolutional Neural Networks for Emotion and Gender Classification 中提出的两个模型,分别是SimpleCNN和MiniXception。利用 imdb_crop

8 Mar 11, 2022
Subpopulation detection in high-dimensional single-cell data

PhenoGraph for Python3 PhenoGraph is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") repr

Dana Pe'er Lab 42 Sep 05, 2022
PyTorch implementation of "Debiased Visual Question Answering from Feature and Sample Perspectives" (NeurIPS 2021)

D-VQA We provide the PyTorch implementation for Debiased Visual Question Answering from Feature and Sample Perspectives (NeurIPS 2021). Dependencies P

Zhiquan Wen 19 Dec 22, 2022
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning This repository provides an implementation of the paper Beta S

Yongchan Kwon 28 Nov 10, 2022
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
Hyperparameter tuning for humans

KerasTuner KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily c

Keras 2.6k Dec 27, 2022
[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer

BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer Project Page | Paper | Video State-of-the-art image-to-image translatio

47 Dec 06, 2022
Pre-trained NFNets with 99% of the accuracy of the official paper

NFNet Pytorch Implementation This repo contains pretrained NFNet models F0-F6 with high ImageNet accuracy from the paper High-Performance Large-Scale

Benjamin Schmidt 133 Dec 09, 2022
Generative Adversarial Text to Image Synthesis

Text To Image Synthesis This is a tensorflow implementation of synthesizing images. The images are synthesized using the GAN-CLS Algorithm from the pa

Hao 575 Jan 08, 2023
Using multidimensional LSTM neural networks to create a forecast for Bitcoin price

Multidimensional LSTM BitCoin Time Series Using multidimensional LSTM neural networks to create a forecast for Bitcoin price. For notes around this co

Jakob Aungiers 318 Dec 14, 2022