GastroNet: A robust attention‐based deep learning and cosine similarity feature selection framework for gastrointestinal disease classification from endoscopic images

Abstract

Diseases of the Gastrointestinal (GI) tract significantly affect the quality of human life and have a high fatality rate. Accurate diagnosis of GI diseases plays a pivotal role in healthcare systems. However, processing large amounts of medical image data can be challenging for radiologists and other medical professionals, increasing the risk of inaccurate medical assessments. Computer‐aided Diagnosis systems provide help to doctors for rapid and accurate diagnosis, thus resulting in saving lives. Recently, many techniques are found in the literature that uses deep Convolutional Neural Network (CNN) models for accurate disease classification. However, they have limitations in their ability to detect deformation‐invariant features and lack robustness. The diseased region is highlighted, using attention‐based image generation and superimposition with original images. A lightweight deep CNN model is employed to get significant features. These features are further reduced using a Cosine similarity‐based technique. The proposed framework is assessed using the Kvasir dataset. To verify the effectiveness of the proposed framework, vast experiments are conducted. The overall accuracy of 97.68%, 99.02% precision, 96.37% recall, and an F‐measure of 97.68% are achieved using the 810 significant features. This reduction in features resulted in a significant reduction in classification time. The robustness of the framework can be observed not only in terms of considerable improvement in accuracy, but also in terms of precision as well as recall, and F‐measure.

Emerging Trends and Advances in the Diagnosis of Gastrointestinal Diseases

“…This method achieved 96.43% accuracy, respectively. A summary of deep learning based techniques which were used for the prediction of peptic ulcer from endoscopic images dataset [59][60][61][62][63] are shown in Table 4.…”

Explainable AI-driven model for gastrointestinal cancer classification

“…CNN Models are optimized for the goal of identifying gastrointestinal lesions like esophagitis, poylps and ulcerative-colitis by the application of enhancements and fine-tuning procedures. These improvements improve the precision and robustness of the models as compared with the latest techniques like (38)(39)(40)(41)(42). Creation of an Ensemble Model: By combining the predictions of every CNN model, the research project suggests and creates an ensemble model.…”

Effect of roasting process on the V (anti-tumor agent) recovery from the slag of the electric arc furnace (EAF)

“…One such avenue lies in the utilization of steel slag, a byproduct of steel manufacturing processes, as a reservoir of V. The mechanism of V compounds in cancer treatment involves their ability to induce apoptosis (programmed cell death) in cancer cells while sparing healthy cells [ 7 , 8 ]. Additionally, V compounds have been shown to inhibit angiogenesis (the formation of new blood vessels) in tumors, thereby impeding their growth and metastasis [ 9 , 10 ]. Furthermore, V-based drugs have exhibited potential in modulating cellular signaling pathways involved in cancer progression, including the PI3K/Akt/mTOR pathway [ [11] , [12] , [13] ].…”