Gastrointestinal Endoscopic Image Classification using a Novel Wavelet Decomposition Based Deep Learning Algorithm

More than 11% of Americans are affected by diseases related to the gastrointestinal (GI) tract. GI endoscopy is an established imaging modality for diagnostic and therapeutic procedures. Large volumes of images and videos generated during this procedure, makes image interpretation cumbersome and varies among physicians. Artificial intelligence (AI) assisted Computer-Aided Diagnosis (CAD) system for digital GI endoscopy is gaining attention that can disrupt GI practice. Several studies have reported the application of computer vision and machine learning algorithms in GI endoscopy. Endoscopic images of varying anatomic features of the Gi tract, challenges their accurate classification. Therefore, a need exists in accurately classifying different GI endoscopic images for upstream processing in the diagnostic platform for digital GI endoscopy. The purpose of this work was to develop a deep learning model using convolutional neural network (CNN) and wavelet decomposed CNN for improved accuracy using publically available GI endoscopic images from Kvasir dataset with 8 different image groups namely Z-line, Pylorus, Cecum, Esophagitis, Polyps, Ulcerative Colitis, Dyed and Lifted Polyps & Dyed Resection Margins. Wavelet decomposition along with CNN architecture allows utilization of spectral information which is mostly lost in conventional CNNs that can enhance model performance. The models were trained with 80% images and 20% were used for testing and accuracy was compared. 10% improvement in accuracy for multi-class classification was observed with wavelet CNN model compared to conventional CNN. The results indicate the potential of image decomposition methods for enhancing digital GI endoscopic procedures.