Classification of Celiac disease using ensemble SMOTE-RF approach

There are emerging challenges in the medical field as artificial intelligence is being introduced for automated detection of different diseases including celiac disease (CD). Manual interpretation is time time-consuming task performed by endoscopists whereas experience plays a key role in figuring out the abnormal region. The endoscopy images are processed with automated detection as their results are promising in terms of presence and non-presence of the disease. Initially, fastNLmeans denoising enabled pre-processing with Synthetic Minority Oversampling Technique (SMOTE) is utilized for mitigating the effect of the noise and for increasing the minority class images. For effective automatic classification, segmentation is a crucial step in image processing. Then, the Sobel operator with Multilevel Otsu thresholding is used as a segmentation step to reduce the complexity of the image. Image spatial features are extracted using a hybrid approach of discrete wavelet transform and high-order spectra (HOS). Thereafter, the Balanced random forest classifier is implemented to classify images as normal and abnormal. This model achieves remarkable performance with an accuracy of 89.41%, precision of 94.23%, recall of 89.09%, and F1 score of 91.58%. This approach validates the results with the help of endoscopists to prove its efficacy. These results outperform by imparting a high value of precision and F1 score.