Fusing compressed deep ConvNets with a self-normalizing residual block and alpha dropout for a cost-efficient classification and diagnosis of gastrointestinal tract diseases

The challenging task of diagnosing gastrointestinal (GI) tracts recently became a popular research topic, where most researchers performed extraordinary feats using numerous deep learning (DL) and computer vision techniques to achieve state-of-the-art (SOTA) diagnostic performance based on accuracy. However, most proposed methods relied on combining complex computational methods and algorithms, causing a significant increase in production difficulty, parameter size, and even training cost. Therefore, this method proposes a straightforward approach to developing a vision-based DL model without requiring heavy computing resources or reliance on other complex feature processing and learning algorithms. This paper included the step-by-step procedure consisting of network compression, layer-wise fusion, and the addition of a modified residual layer (MResBlock) with a self-normalizing attribute and a more robust regularization. In addition, the paper also presents the performance of the proposed method toward the diagnosis of four GI tract conditions, including polyps, ulcers, esophagitis, and healthy mucosa. The paper concludes that the proposed method did radiate a significant improvement in the overall performance, cost-efficiency, and especially practicality compared to most current SOTA methods. • The proposed method combined profound techniques like feature fusion, residual learning, and self-normalization to develop a lightweight model that accurately diagnoses gastrointestinal (GI) tract conditions. • The model produced from the proposed method generated better performance than most pre-existing state-of-the-art Deep Convolutional Neural Networks that diagnosed the presented four GI tract conditions. • Aside from its competitive performance, the model based on the proposed method only had 1.2M parameters and only consumed 1.5 GFLOPS, making it significantly more cost-efficient than most existing solutions.