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Ensembles of Deep Learning Framework for Stomach Abnormalities Classification

Computers, Materials & Continua 2022年第3期70卷 4357-4372页

作者： Talha Saeed Chu Kiong Loo Muhammad Shahreeza Safiruz Kassim Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyUniversiti Malaya50603 Kuala LumpurMalaysia

Abnormalities of the gastrointestinal tract are widespread worldwide ***,an effective way to diagnose these life-threatening diseases is based on endoscopy,which comprises a vast number of ***,the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the ***,this led to the rise of studies on designingAI-based systems to assist physicians in the *** several medical imaging tasks,deep learning methods,especially convolutional neural networks(CNNs),have contributed to the stateof-the-art outcomes,where the complicated nonlinear relation between target classes and data can be learned and not limit to hand-crafted *** the other hand,hyperparameters are commonly set manually,which may take a long time and leave the risk of non-optimal hyperparameters for *** effective tool for tuning optimal hyperparameters of deep CNNis bayesian ***,due to the complexity of the CNN,the network can be regarded as a black-box model where the information stored within it is hard to ***,Explainable Artificial Intelligence(XAI)techniques are applied to overcome this issue by interpreting the decisions of the CNNs in such wise the physicians can *** play an essential role in real-time medical diagnosis,CNN-based models need to be accurate and interpretable,while the uncertainty must be ***,a novel method comprising of three phases is proposed to classify these life-threatening *** first,hyperparameter tuning is performed using bayesian optimization for two state-of-the-art deep CNNs,and then Darknet53 and InceptionV3 features are extracted from these fine-tunned ***,XAI techniques are used to interpret which part of the images CNN takes for feature *** last,the features are fused,and uncertainties are handled by selecting entropybased *** experimental results show that the

关键词： Gastrointestinal tract deep learning bayesian optimization hyperparameters explainable AI uncertainty handling feature fusion

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