Yacob Yasmin Mohd, Alquran Hiam, Mustafa Wan Azani, Alsalatie Mohammed, Sakim Harsa Amylia Mat, Lola Muhamad Safiih
Faculty of Electronic Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia.
Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia.
Diagnostics (Basel). 2023 Jan 17;13(3):336. doi: 10.3390/diagnostics13030336.
Atrophic gastritis (AG) is commonly caused by the infection of the () bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on -associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.
萎缩性胃炎（AG）通常由（）细菌感染引起。如果不进行治疗，AG可能会发展成慢性病，进而导致胃癌，而胃癌被认为是全球癌症相关死亡的第三大主要原因。对AG进行早期检测对于避免此类情况至关重要。这项工作聚焦于胃窦处的相关感染，此处的分类为正常与萎缩性胃炎的二元分类。现有工作开发了具有22层预训练模型的GoogLeNet深度卷积神经网络（DCNN）。另一项研究采用了基于Inception模块的GoogLeNet、快速鲁棒模糊C均值（FRFCM）和简单线性迭代聚类（SLIC）超像素算法来识别胃部疾病。带有Caffe框架的GoogLeNet和ResNet - 50是用于检测感染的机器学习模型。然而，随着网络深度的增加，准确率可能会降低。人们迫切期待对当前标准方法进行升级，以避免可能导致慢性AG的未治疗和不准确诊断。所提出的工作纳入了改进技术，这些技术围绕DCNN展开，采用池化作为预训练模型，并进行通道混洗，以辅助跨特征通道的信息流，从而简化深度卷积神经网络的网络训练。此外，典型相关分析（CCA）特征融合方法和ReliefF特征选择方法旨在改进组合技术。CCA对预训练的ShuffleNet生成的两个重要特征数据集之间的关系进行建模。ReliefF从CCA中减少并选择基本特征，并使用广义相加模型（GAM）进行分类。据信，扩展后的工作在测试准确率达到98.2%的情况下是合理的，从而能够准确诊断正常与萎缩性胃炎。
