Medical Image Classification Algorithm Based on Visual Attention Mechanism‐MCNN

F, An; Li, Xiaowei; Ma, Xingmin

doi:10.1155/2021/6280690

Cited by 27 publications

(20 citation statements)

References 50 publications

(70 reference 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%

Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

S.¹,

Daramola²

2021

Artificial Intelligence

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The employment of machine learning algorithms in disease classification has evolved as a precision medicine for scientific innovation. The geometric growth in various machine learning systems has paved the way for more research in the medical imaging process. This research aims to promote the development of machine learning algorithms for the classification of medical images. Automated classification of medical images is a fascinating application of machine learning and they have the possibility of higher predictability and accuracy. The technological advancement in the processing of medical imaging will help to reduce the complexities of diseases and some existing constraints will be greatly minimized. This research exposes the main ensemble learning techniques as it covers the theoretical background of machine learning, applications, comparison of machine learning and deep learning, ensemble learning with reviews of state-of the art literature, framework, and analysis. The work extends to medical image types, applications, benefits, and operations. We proposed the application of the ensemble machine learning approach in the classification of medical images for better performance and accuracy. The integration of advanced technology in clinical imaging will help in the prompt classification, prediction, early detection, and a better interpretation of medical images, this will, in turn, improves the quality of life and expands the clinical bearing for machine learning applications.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

S.¹,

Daramola²

2021

Artificial Intelligence

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“…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%

INTRPRT: A Systematic Review of and Guidelines for Designing and Validating Transparent AI in Medical Image Analysis

Chen¹,

Gómez²,

Huang³

et al. 2021

Preprint

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Transparency in Machine Learning (ML), including interpretable or explainable ML, attempts to reveal the working mechanisms of complex models, including deep neural networks. Transparent ML promises to advance human factors engineering goals of human-centered AI, such as increasing trust or reducing automation bias, in the target users. However, a core requirement in achieving these aims is that the method is indeed transparent to the users. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i. e., a relationship between algorithm and user; as a result, iterative prototyping and evaluation with users is critical to attaining adequate solutions that afford transparency. However, following human-centered design principles in highly specialized and high stakes domains, such as healthcare and medical image analysis, is challenging due to the limited availability of and access to end users, such as domain experts, providers, or patients. This dilemma is further exacerbated by the high knowledge imbalance between ML designers and those end users, which implies an even greater need for iterated development.To investigate the state of transparent ML in medical image analysis, we conducted and now present a systematic review of the literature. Our review reveals multiple severe shortcomings in the design and validation of transparent ML for medical image analysis applications. We find that most studies to date approach transparency as a property of the model itself, similar to task performance, without considering end users during neither development nor evaluation. Despite the considerable difference between the roles and knowledge of ML developers and clinical stakeholders, no study reported formative user research to inform the design and development of transparent ML models; moreover, only a few studies validated transparency claims through empirical user evaluations. The oversight of considering ML transparency as a relationship with end users, the lack of user research, and the sporadic validation of transparency claims put contemporary research on transparent ML for medical image analysis at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research while acknowledging the challenges of human-centered design in healthcare, we introduce the INTRPRT guideline, a systematic design directive for transparent ML systems in medical image analysis. To bridge the disconnect between ML system designers and end users and avoid misspending costly development efforts on models that are finally validated as non-transparent to end users, the INTRPRT guideline suggests formative user research as the first step of transparent model design to understand user needs and domain requirements. Following this process produces evidence to support * Joint first authors.Preprint. Under review.

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“…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%

Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition

Gao¹,

Cai²,

Shi³

et al. 2021

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High complexity and low recognition rate are two common problems with the current finger vein recognition methods. To solve these problems, this paper integrates two-dimensional kernel principal component analysis (K2DPCA) plus two-dimensional linear discriminant analysis (2DLDA) (K2DPCA+2DLDA) into convolutional neural network (CNN) to recognize finger veins. Considering the row and column correlations of the finger vein image matrix and the classes of finger vein images, the authors adopted K2DPCA and 2DLDA separately for dimensionality reduction and extraction of nonlinear features in row and column directions, producing a dimensionally reduced compressed image without row or column correlation. Taking the dimensionally reduced compressed image as the input, the CNN was introduced to learn higher-level features, making finger vein recognition more accurate and robust. The public dataset of Finger Vein USM (FV-USM) Database was adopted for experimental verification. The results show that the proposed approach effectively overcome the common defects of original image feature extraction: the insufficient feature description, and the redundancy of information. When the training reached 120 epochs, the model basically realized stable convergence, with the loss approaching zero and the recognition rate reaching 97.3%. Compared with two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA), our strategy, which integrates K2DPCA+2DLDA with CNN, achieved a very high recognition rate of finger vein images.

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Medical Image Classification Algorithm Based on Visual Attention Mechanism‐MCNN

Cited by 27 publications

References 50 publications

Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

INTRPRT: A Systematic Review of and Guidelines for Designing and Validating Transparent AI in Medical Image Analysis

Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition

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