Computer-Aided Diagnosis using Class-Weighted Deep Neural Network.

Deep neural networks are increasingly being used for computer-aided diagnosis, but erroneous diagnoses can be extremely costly for patients. ... LDU was evaluated on the diagnosis of myocardial infarction (using discharge summaries), the diagnosis of any comorbidities (using structured data), and the diagnosis of pleural effusion and pneumothorax ... (x " ) = − ∑ P # (x " ) $ #%& log P # (x " ) (I) where K is the number of deep neural networks in the ensemble and P # (x " ) is the probability of a positive diagnosis computed by the k-th neural network ...

arXiv:2108.07392v5 fatcat:ksmpfh3b6zgb7nbp6a4hepc3ya

Multiple Versions

The application area covers the whole spectrum of medical image analysis including detection, segmentation, classification, and computer aided diagnosis. ... Recently, deep learning methods utilizing deep convolutional neural networks have been applied to medical image analysis providing promising results. ... Computer Aided Diagnosis (CADx) The computer aided detection (CADe) or computer aided diagnosis (CADx) system is used in radiology that assists the radiologist and clinical practitioners in interpreting ...

arXiv:1709.02250v1 fatcat:mlk3vdn7ibggvcxzsb7c23kibq

Multiple Versions

Therefore, it can be more convenient to use deep neural network technology in computer aided detection systems for mass detection from breast ultrasonography images. ... In this study, mass detection from breast ultrasonography images was realized using deep neural networks. ... These technologies include different classification structures and are called computer-aided diagnosis (CAD) systems. These classifier structures can be based on fuzzy logic and deep neural networks. ...

doi:10.17776/csj.691683 fatcat:vac75zjn3jhu5agmdfx33rh724

DOAJ

Automated detection of lung abnormalities has a significant role in the computer aided diagnosis of lung diseases. ... Recently, medical image analysis utilizes Convolution Neural Network(CNN) to improve the outcome of clinical diagnosis. ... Deep learning techniques and Deep neural networks have overcome the need for feature extraction and feature selection stages and can be used to implement a fully automated computer aided diagnosis system ...

doi:10.17762/itii.v9i1.93 fatcat:jneie2xzprfcdomugmleaxnyou

Computer-aided pathology to analyze microscopic histopathology images for diagnosis with an increasing number of breast cancer patients can bring the cost and delays of diagnosis down. ... The convolutional neural network, a deep learning framework, provides remarkable results in tissue images analysis, but lacks in providing interpretation and reasoning behind the decisions. ... ACKNOWLEDGMENT Authors would like to thank Nvidia Corporation for donation of GPUs used for this research. ...

arXiv:2003.00823v1 fatcat:24hiurtuifbclchuxhpj4lp4ta

Specifically, our approach leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) for glaucoma diagnosis and localization, respectively. ... In this paper, we present a novel computational approach towards glaucoma diagnosis and localization, only making use of eye fundus images that are analyzed by state-of-the-art deep learning techniques ... First, few medical image datasets are available with a volume sufficient to train a deep neural network from scratch, especially given that, by convention, the deeper the neural networks constructed, the ...

arXiv:1812.02405v2 fatcat:yrpka44c4bd5jpsxn576wcut3i

Multiple Versions

Deep neural networks have revolutionized medical image analysis and disease diagnosis. ... Bayesian neural networks provide a principled approach for modelling uncertainty and increasing patient safety, but they have a large computational overhead and provide limited improvement in calibration ... Introduction In computer-aided diagnosis, AI models must not only be accurate, but they should also indicate when they are likely to be incorrect. ...

arXiv:2005.11797v2 fatcat:q7dwjwlcvvb5fpr3aa5j23fiqm

Multiple Versions

using a Base 36 numbering system employing both numerals and letters. ... Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library. ... [10575-71] 10575 21 Compression of deep convolutional neural network for computer-aided diagnosis of masses in digital breast tomosynthesis [10575-72] 10575 22 ICADx: interpretable computer aided ...

doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Patches that do not share the characteristics of this normal population are detected and analyzed by oneclass support vector machine and 1-layer neural network. ... Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. ... Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. ...

doi:10.22214/ijraset.2019.5443 fatcat:xogbkgdiijex3cmnfvrgabq6ua

Open Access

This paper proposes an approach to use computer-aided techniques in deep learning neural networks such as Convolutional neural networks (CNN) and Residual Neural Networks (ResNet) to predict skin diseases ... Since the human analysis of such diseases takes some time and effort, and current methods are only used to analyse singular types of skin diseases, there is a need for a more high-level computer-aided ... CNN is one of the types of neural network which is highly used in the computer science field. ...

doi:10.35940/ijrte.f9038.038620 fatcat:mytjmukmd5cunlrjnlutanu63i

Open Access

State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. ... To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. ... While previous classifiers mostly used shallow neural networks, recent years witnessed great advancement on applying deep learning to computer aided detection. Wang et al. ...

arXiv:1803.01906v1 fatcat:2465qphxajchzegpic57qe3cfu

) to build Computer Aided Diagnosis (CAD) systems. ... In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published ... The network weights were initialized using He norm initialization. ...

doi:10.1007/978-3-319-93000-8_62 fatcat:twnfkl2jdfgqpioplbomlb4tji

10 target classes with imbalanced samples, using deep learning classification models. ... The testing results showed that the developed deep neural network model achieved an accuracy of 88.02%, a recall of 84.30%, a precision of 85.01%, and an F-score of 0.8508 in average. ... During the testing process, the deep neural network is only used with the scaled down weights (or partial weights in the network) instead of using entire neural units. ...

doi:10.14569/ijacsa.2018.090501 fatcat:v7chiqftgbhktlnonzf4fvgrr4

Szczepanski

Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography. ... In this paper, we incorporate the attention mechanism into a deep convolutional neural network, and thus propose the ChestNet model to address effective diagnosis of thorax diseases on chest radiography ... Acknowledge We appreciate the efforts devoted to collect and share the ChestX-ray14 dataset for comparing the approaches to the diagnosis of 14 thorax diseases on chest radiographs. ...

arXiv:1807.03058v1 fatcat:jtxjxuy4f5dttjuf4ul6fkf2ve

Keywords: Computer aided diagnosis, tissue pattern, biopsy ... Abstract: Computer aided diagnosis (CAD) is one of the potential technologies in today's medical world that assist doctors to interpret and evaluate medical images in a short time. ... Thus, computer-aided diagnosis systems are needed to guide clinicians. ...

doi:10.22214/ijraset.2022.40524 fatcat:aoisxnkcxjhxfbpxbwn7ykwlaa

Open Access

Incorporating Uncertainty in Learning to Defer Algorithms for Safe Computer-Aided Diagnosis [article]

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Medical Image Analysis using Convolutional Neural Networks: A Review [article]

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Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images

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CUSTOMIZED CONVOLUTION NEURAL NETWORK FOR MULTI-CLASS LUNG ABNORMALITY CLASSIFICATION FROM CT IMAGES

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Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning [article]

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Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning [article]

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Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis [article]

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Front Matter: Volume 10575

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Breast Cancer Detection using Deep Learning

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Skin Diseases Prediction using Deep Learning Framework

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Abnormality Detection in Mammography using Deep Convolutional Neural Networks [article]

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Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs [chapter]

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Cardiotocographic Diagnosis of Fetal Health based on Multiclass Morphologic Pattern Predictions using Deep Learning Classification

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ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography [article]

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A Literature Review on Techniques for Detection of Lung Diseases

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