Hybrid model-based early diagnosis of esophageal disorders using convolutional neural network and refined logistic regression

Abstract

Abstract Accurate diagnosis of the stage of esophageal disorders is crucial in the treatment planning for patients with esophageal cancer and in improving the 5-year survival rate. The progression of esophageal cancer typically begins with precancerous esophageal disorders such as gastroesophageal reflux disease (GERD), esophagitis, and non-dysplasia Barrett’s esophagus, eventually advancing to low- and high-dysplasia Barrett’s esophagus and ultimately to esophageal adenocarcinoma (EAC). The majority of prior research efforts have primarily focused on the identification of general gastrointestinal (GI) tract diseases and the detection of esophageal cancer, with limited attention to the diverse spectrum of esophageal disorders. To address this gap, an innovative framework called Hybrid Model-Based Esophageal Disorder Diagnosis (HMEDD) is developed in this work. The primary goal of HMEDD is to enable early diagnosis of various esophageal disorders using gastroscopic images. HMEDD combines the feature extraction capabilities of an Esophageal Convolutional Neural Network (EsoNet) with the high classification accuracy of a Refined Logistic Regression (RLR) model. EsoNet comprises 14 weight layers and kernels $$\left(3\times 3\right)$$ 3 × 3 used for high-level deep feature learning. Esophageal disorders are classified using the RLR model, which is developed by fine-tuning hyperparameters in the traditional Logistic Regression (LR) model using Random Search Cross-Validation (RandomizedSearchCV). HMEDD is extensively validated using a data set containing numerous esophageal abnormalities captured through gastroscopic images. The results of this work demonstrate the effectiveness of HMEDD in accurately classifying different esophageal disorders, with an impressive accuracy of 92.15%. These findings will assist physicians in the accurate early diagnosis of esophageal disorders, ultimately preventing their progression to cancer.

Keywords

• Gastroscopic images
• Esophageal disorder classification
• Deep learning
• HMEDD
• EsoNet
• RLR