35 research outputs found
Classification of Chest Diseases using Wavelet Transforms and Transfer Learning
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.Comment: 8 pages, 4 figures, Presented in International Conference On Medical
Imaging And Computer-Aided Diagnosis (MICAD 2020), proceeding will be
published with Springer in their "Lecture Notes in Electrical Engineering
(LNEE)" (ISSN: 1876-1100
Explainable Disease Classification via weakly-supervised segmentation
Deep learning based approaches to Computer Aided Diagnosis (CAD) typically
pose the problem as an image classification (Normal or Abnormal) problem. These
systems achieve high to very high accuracy in specific disease detection for
which they are trained but lack in terms of an explanation for the provided
decision/classification result. The activation maps which correspond to
decisions do not correlate well with regions of interest for specific diseases.
This paper examines this problem and proposes an approach which mimics the
clinical practice of looking for an evidence prior to diagnosis. A CAD model is
learnt using a mixed set of information: class labels for the entire training
set of images plus a rough localisation of suspect regions as an extra input
for a smaller subset of training images for guiding the learning. The proposed
approach is illustrated with detection of diabetic macular edema (DME) from OCT
slices. Results of testing on on a large public dataset show that with just a
third of images with roughly segmented fluid filled regions, the classification
accuracy is on par with state of the art methods while providing a good
explanation in the form of anatomically accurate heatmap /region of interest.
The proposed solution is then adapted to Breast Cancer detection from
mammographic images. Good evaluation results on public datasets underscores the
generalisability of the proposed solution