Pneumonia Detection using machine learning - with PyTorch

Last update: Jul 07, 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.

Pneumonia Detection using machine learning - with PyTorch

Related tags

Overview

Pneumonia Detection

Data

Processing and Augmentation

Model

Visualization using Streamlit

Owner

Wilhelm Berghammer

Pytorch implementation for "Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion" (NeurIPS 2021)

Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

The official implementation of paper "Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks" (IJCV under review).

Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

Optimized code based on M2 for faster image captioning training

This repo is the code release of EMNLP 2021 conference paper "Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories".

Indices Matter: Learning to Index for Deep Image Matting

Project repo for the paper SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition

Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling

[제 13회 투빅스 컨퍼런스] OK Mugle! - 장르부터 멜로디까지, Content-based Music Recommendation

Self-driving car env with PPO algorithm from stable baseline3

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

Drone Task1 - Drone Task1 With Python

Code and Data for the paper: Molecular Contrastive Learning with Chemical Element Knowledge Graph [AAAI 2022]

🛠️ SLAMcore SLAM Utilities

OpenMMLab Computer Vision Foundation

Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting

Hierarchical Uniform Manifold Approximation and Projection