Abstract
BackgroundThe early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract.ObjectiveThis study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases.MethodsOur proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment.ResultsAll the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods.ConclusionsThis study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.
Highlights
Various types of gastrointestinal (GI) disorders, such as a tumors, ulcerative colitis, irritable bowel syndrome, hemorrhoids, Helicobacter pylori, Crohn disease, polyps, and colorectal cancer, are among the leading causes of death [1]
Our proposed classification network shows the best performance for this data set, and it is capable of classifying a large number of classes
We evaluated the performance of our selected data set using 3 conventional handcrafted feature extraction methods—local binary pattern (LBP) [67], histogram of oriented gradients (HOG) [68], and multilevel local binary pattern (MLBP) [69]—with 4 different classifiers—AdaBoostM2 [32], Multi-support vector machine (SVM) [33], random forest (RF) [70], and k-nearest neighbors (KNN) [31]
Summary
Various types of gastrointestinal (GI) disorders, such as a tumors, ulcerative colitis, irritable bowel syndrome, hemorrhoids, Helicobacter pylori, Crohn disease, polyps, and colorectal cancer, are among the leading causes of death [1]. A CAD model analyzes the visual data (obtained in the first step) to highlight the lesions or suspicious regions These highlighted results further assist the medical experts in making an effective diagnostic decision in a short time. Various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases Most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. The retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database These past cases help the medical expert validate the current computer prediction subjectively, which results in better diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences
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