AlexNet CNN as the base model is used to classify CXR images as Normal / Tuberculosis_infected through Transfer learning.
Data Source: https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset
- Total File Size: 700MB
- Number of records: 3500 Normal, 700 Tuberculosis (Separated into 2 (3600 train and 600 validation) parts)
- Images are size of (512 * 512 * 3) and in .jpg format.
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Layer (type) Output Shape Param
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Conv2d-1 [18, 64, 127, 127] 23,296
ReLU-2 [18, 64, 127, 127] 0
MaxPool2d-3 [18, 64, 63, 63] 0
Conv2d-4 [18, 192, 63, 63] 307,392
ReLU-5 [18, 192, 63, 63] 0
MaxPool2d-6 [18, 192, 31, 31] 0
Conv2d-7 [18, 384, 31, 31] 663,936
ReLU-8 [18, 384, 31, 31] 0
Conv2d-9 [18, 256, 31, 31] 884,992
ReLU-10 [18, 256, 31, 31] 0
Conv2d-11 [18, 256, 31, 31] 590,080
ReLU-12 [18, 256, 31, 31] 0
MaxPool2d-13 [18, 256, 15, 15] 0
AdaptiveAvgPool2d-14 [18, 256, 15, 15] 0
Dropout-15 [18, 57600] 0
Linear-16 [18, 510] 29,376,510
ReLU-17 [18, 510] 0
Linear-18 [18, 1] 511
================================================================
Total params: 31,846,717
Trainable params: 29,377,021
Non-trainable params: 2,469,696
- network uses pretrained weights and during the training, CNN weights are freezed and training only affects the Linear layers.
- For solving the problem of imbalance dataset, weighted classes used alongside with cross entropy loss
Validation loss: 0.019457, Validation acc: 99.333333,
for class Normal:
validation precision: 0.994024, validation recall: 0.998000 , validation F1: 0.996008
for class Tuberculosis:
validation precision: 0.989796, validation recall: 0.970000 , validation F1: 0.979798
Confusion matrix:
