Enhancing Medical Image Analysis through Geometric and Photometric transformations
This addresses data scarcity in medical imaging for practitioners, but is incremental as it applies existing augmentation methods to new datasets.
The paper tackled the problem of limited labeled data in medical image analysis by evaluating data augmentation techniques on skin cancer classification and retinal blood vessel segmentation, achieving accuracy improvements from 90.74% to 96.88% and Dice coefficient increases from 0 to 0.4163 respectively.
Medical image analysis suffers from a lack of labeled data due to several challenges including patient privacy and lack of experts. Although some AI models only perform well with large amounts of data, we will move to data augmentation where there is a solution to improve the performance of our models and increase the dataset size through traditional or advanced techniques. In this paper, we evaluate the effectiveness of data augmentation techniques on two different medical image datasets. In the first step, we applied some transformation techniques to the skin cancer dataset containing benign and malignant classes. Then, we trained the convolutional neural network (CNN) on the dataset before and after augmentation, which significantly improved test accuracy from 90.74% to 96.88% and decreased test loss from 0.7921 to 0.1468 after augmentation. In the second step, we used the Mixup technique by mixing two random images and their corresponding masks using the retina and blood vessels dataset, then we trained the U-net model and obtained the Dice coefficient which increased from 0 before augmentation to 0.4163 after augmentation. The result shows the effect of using data augmentation to increase the dataset size on the classification and segmentation performance.