Classification of Chest Diseases using Wavelet Transforms and Transfer Learning
This work addresses the need for more efficient diagnostic tools for radiologists in detecting chest diseases, but it is incremental as it builds on existing deep learning and image processing techniques.
The paper tackled the problem of classifying chest diseases from X-ray scans by combining wavelet transforms for feature enhancement with transfer learning for classification, achieving significant improvement in results on the ChestX-ray14 database with 14 labeled diseases.
Chest X-ray scan is a most often used modality by radiologists to diagnose many chest related diseases in their initial stages. The proposed system aids the radiologists in making decision about the diseases found in the scans more efficiently. Our system combines the techniques of image processing for feature enhancement and deep learning for classification among diseases. We have used the ChestX-ray14 database in order to train our deep learning model on the 14 different labeled diseases found in it. The proposed research shows the significant improvement in the results by using wavelet transforms as pre-processing technique.