CapsuleNet: A Deep Learning Model To Classify GI Diseases Using EfficientNet-b7
This work addresses the need for automated analysis to reduce doctor workload and increase diagnostic accuracy in gastrointestinal disease classification, though it is incremental as it builds on existing CNN methods.
The paper tackled the problem of classifying 10 distinct gastrointestinal abnormalities from capsule endoscopy images using a deep learning model, achieving a micro accuracy of 84.5% and outperforming a VGG16 baseline.
Gastrointestinal (GI) diseases represent a significant global health concern, with Capsule Endoscopy (CE) offering a non-invasive method for diagnosis by capturing a large number of GI tract images. However, the sheer volume of video frames necessitates automated analysis to reduce the workload on doctors and increase the diagnostic accuracy. In this paper, we present CapsuleNet, a deep learning model developed for the Capsule Vision 2024 Challenge, aimed at classifying 10 distinct GI abnormalities. Using a highly imbalanced dataset, we implemented various data augmentation strategies, reducing the data imbalance to a manageable level. Our model leverages a pretrained EfficientNet-b7 backbone, tuned with additional layers for classification and optimized with PReLU activation functions. The model demonstrated superior performance on validation data, achieving a micro accuracy of 84.5% and outperforming the VGG16 baseline across most classes. Despite these advances, challenges remain in classifying certain abnormalities, such as Erythema. Our findings suggest that CNN-based models like CapsuleNet can provide an efficient solution for GI tract disease classification, particularly when inference time is a critical factor.