A two-phase transfer learning framework for gastrointestinal diseases classification DOI Creative Commons
Adnan Ali, Arshad Iqbal, Sohail Ahmed Khan

et al.

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2587 - e2587

Published: Dec. 19, 2024

Gastrointestinal (GI) disorders are common and often debilitating health issues that affect a significant portion of the population. Recent advancements in artificial intelligence, particularly computer vision algorithms, have shown great potential detecting classifying medical images. These algorithms utilize deep convolutional neural network architectures to learn complex spatial features images make predictions for similar unseen The proposed study aims assist gastroenterologists making more efficient accurate diagnoses GI patients by utilizing its two-phase transfer learning framework identify diseases from endoscopic Three pre-trained image classification models, namely Xception, InceptionResNetV2, VGG16, fine-tuned on publicly available datasets annotated tract. Additionally, two custom networks constructed fully trained comparative analysis their performance. Four different tasks examined based categories. architecture employing InceptionResNetV2 achieves most consistent generalized performance across tasks, yielding accuracy scores 85.7% general tract (eight-category classification), 97.6% three-diseases classification, 99.5% polyp identification (binary 74.2% binary esophagitis severity results indicate effectiveness clinical use enhance diseases, aiding early diagnosis treatment.

Language: Английский

A two-phase transfer learning framework for gastrointestinal diseases classification DOI Creative Commons
Adnan Ali, Arshad Iqbal, Sohail Ahmed Khan

et al.

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2587 - e2587

Published: Dec. 19, 2024

Gastrointestinal (GI) disorders are common and often debilitating health issues that affect a significant portion of the population. Recent advancements in artificial intelligence, particularly computer vision algorithms, have shown great potential detecting classifying medical images. These algorithms utilize deep convolutional neural network architectures to learn complex spatial features images make predictions for similar unseen The proposed study aims assist gastroenterologists making more efficient accurate diagnoses GI patients by utilizing its two-phase transfer learning framework identify diseases from endoscopic Three pre-trained image classification models, namely Xception, InceptionResNetV2, VGG16, fine-tuned on publicly available datasets annotated tract. Additionally, two custom networks constructed fully trained comparative analysis their performance. Four different tasks examined based categories. architecture employing InceptionResNetV2 achieves most consistent generalized performance across tasks, yielding accuracy scores 85.7% general tract (eight-category classification), 97.6% three-diseases classification, 99.5% polyp identification (binary 74.2% binary esophagitis severity results indicate effectiveness clinical use enhance diseases, aiding early diagnosis treatment.

Language: Английский

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