Hi
I’m currently working on a classification model whose input are x-ray images, i need to classify it into the respective disease categories. I have trained resnet 18 (not pretrained )on a data-set of 100 images per class.
I have 1050 images for training and 450 images for testing (70:30 split).
On running for 50 epochs the accuracy goes to 92 percent, but the testing accuracy isn’t good at all it is just about 15 %, i think the model is overfitting,
can anyone suggest as to what can be done to improve the testing accuracy?
I have grey scale images and i have down-scaled them to 224 x 224 so image shape is [1,224,224].
I have modified the first conv2d layer to accept grey scale images and last layer of resnet to output the required number of classes.
I’m a beginner, it would great if anyone can help with this.