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. This is just to visualize.