2021
DOI: 10.1155/2021/6280690
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Medical Image Classification Algorithm Based on Visual Attention Mechanism‐MCNN

Abstract: Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical… Show more

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Cited by 28 publications
(20 citation statements)
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“…The pooling layers also holds a filter and it moves over the input but may not have weight. The pooling is sub-divided into Max and Average pooling with the functions of calculating the maximum and or average value respectively [28]. The fully-connected layer, output layers are fully joined via a node to former layer and do classification tasks through the feature extracted from the preceding layer.…”
Section: Convolutional Neural Networkmentioning
confidence: 99%
“…The pooling layers also holds a filter and it moves over the input but may not have weight. The pooling is sub-divided into Max and Average pooling with the functions of calculating the maximum and or average value respectively [28]. The fully-connected layer, output layers are fully joined via a node to former layer and do classification tasks through the feature extracted from the preceding layer.…”
Section: Convolutional Neural Networkmentioning
confidence: 99%
“…The complex nature of both 3D imaging in radiology and pathological images makes image analysis tasks more time consuming than 2D image analysis that is more prevalent in other specialities, such as dermatology, which motivates transparency as an alternative to complete human image analysis to save time while retaining trustworthiness. In detail, classification problems in 3D radiological images and pathological images included abnormality detection in computed tomography (CT) scans [3,5,61,47,89,107,111,112], MRIs [34,11,38,51,59,87,85,50,95,77,78,98,100,104], pathology images [1,24,26,27,30,34,37,40,82,84,50,74,76,108,5] and positron emission tomography (PET) images [68]. Mammography dominated the 2D radiology image applications [60,88,44,45,86,96,99,…”
Section: T: Taskmentioning
confidence: 99%
“…In recent years, deep learning (DL) [21] has made great breakthroughs in the fields of computer vision, such as image classification [22][23][24], image recognition, image detection [25], image segmentation [26], etc. Notably, convolutional neural network (CNN) [27] achieved remarkable success in many aspects, ranging from human action identification action identification [28], signal reconstruction [29,30], and other applications.…”
Section: Introductionmentioning
confidence: 99%