Medical image classification with convolutional neural network

Li, Qing; Cai, Weidong; Wang, Xiaogang; Zhou, Yun; Feng, David Dagan; Chen, Mei

doi:10.1109/icarcv.2014.7064414

Cited by 546 publications

(275 citation statements)

References 22 publications

(29 reference statements)

Supporting

Mentioning

270

Contrasting

Unclassified

Order By: Relevance

“…For computer-aided ILD classification, all previous studies have employed a patch-based image representation with the classification results of moderate success (Depeursinge, Van de Ville et al 2012; Song et al 2013, 2015; Li et al 2014). There are two major drawbacks for the image patch-based methods: (1), The image patch sizes or scales in studies (Song et al 2013, 2015) are relatively small (31 × 31 pixels) where some visual details and spatial context may not be fully captured.…”

Section: Introductionmentioning

confidence: 99%

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Gao

Bağcı

Lü

et al. 2016

Computer Methods in Biomechanics and Biomedical Engineering: Im

213

166

View full text Add to dashboard Cite

Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore, it is important for developing automated pulmonary computer-aided detection systems. Conventionally, this task relies on experts’ manual identification of regions of interest (ROIs) as a prerequisite to diagnose potential diseases. This protocol is time consuming and inhibits fully automatic assessment. In this paper, we present a new method to classify ILD imaging patterns on CT images. The main difference is that the proposed algorithm uses the entire image as a holistic input. By circumventing the prerequisite of manual input ROIs, our problem set-up is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrate state-of-the-art classification accuracy under the patch-based classification and shows the potential of predicting the ILD type using holistic image.

show abstract

Section: Introductionmentioning

confidence: 99%

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Gao

Bağcı

Lü

et al. 2016

Computer Methods in Biomechanics and Biomedical Engineering: Im

213

166

View full text Add to dashboard Cite

show abstract

“…For the use case of classifying interstitial lung diseases, Li et al [8] provide a simple CNN model containing a single convolutional layer. They yield per-class precision and recall between 0.8 and 0.9 for classification into five classes (normal, emphysema, ground glass, fibrosis, and micro-nodules) outperforming the SIFT feature as well as Restricted Boltzmann Machines.…”

Section: Related Workmentioning

confidence: 99%

Learning laparoscopic video shot classification for gynecological surgery

Petscharnig

Schöffmann

2017

Multimed Tools Appl

View full text Add to dashboard Cite

Videos of endoscopic surgery are used for education of medical experts, analysis in medical research, and documentation for everyday clinical life. Hand-crafted image descriptors lack the capabilities of a semantic classification of surgical actions and video shots of anatomical structures. In this work, we investigate how well single-frame convolutional neural networks (CNN) for semantic shot classification in gynecologic surgery work. Together with medical experts, we manually annotate hours of raw endoscopic gynecologic surgery videos showing endometriosis treatment and myoma resection of over 100 patients. The cleaned ground truth dataset comprises 9 h of annotated video material (from 111 different recordings). We use the well-known CNN architectures AlexNet and GoogLeNet and train these architectures for both, surgical actions and anatomy, from scratch. Furthermore, we extract high-level features from AlexNet with weights from a pre-trained model from the Caffe model zoo and feed them to an SVM classifier. Our evaluation shows that we reach an average recall of .697 and .515 for classification of anatomical structures and surgical actions respectively using off-the-shelf CNN features. Using GoogLeNet, we achieve a mean recall of .782 and .617 for classification of anatomical structures and surgical actions respectively. With AlexNet the achieved recall is .615 for anatomical structures and .469 for surgical action classification respectively. The main conclusion of our work is that advances in general image classification methods transfer to the domain of endoscopic surgery videos in gynecology. This is relevant as this domain is different from natural images, e.g. it is distinguished by smoke, reflections, or a limited amount of colors.

show abstract

“…It is also possible to use more datasets in the testing of the AMPSONN method to prove its effectiveness in other application areas. As the field of data analytics and prediction is constantly growing, there is a need to fine-tune new algorithms to adapt to more real-world applications, such as image processing [45] and prediction in the medical field [46].…”

Section: Discussionmentioning

confidence: 99%

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

Lee¹,

Aziz²,

Jaafar³

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Artificial Neural Network (ANN) design has long been a complex problem because its performance depends heavily on the network topology and algorithm to train the set of synaptic weights. Particle Swarm Optimization (PSO) has been the favored optimization algorithm to complement ANN, but a thorough literature study has shown that there are gaps with current approaches which integrate PSO with ANN, including the optimization of network topology and the unreliable weight training process. These gaps have caused inferior effect on critical Artificial Intelligence (AI) applications and systems, particularly when predicting plant machinery and piping failure due to corrosion. The problem of corrosion prediction in the oil and gas domain remains unanswered due to the lack of a flexible prediction method which targets specific damage mechanisms that caused corrosion. This paper proposes a hybrid prediction method known as the Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN), which integrates several layers of PSO to optimize different parameters of the ANN. The multilayered PSO enables the method to optimize the network topology and train the set of synaptic weights at the same time using a hierarchical optimization approach. Through detailed discussion and literature study, the damage mechanism focused in this research is the CO 2 corrosion and the dataset for this research is obtained from the NORSOK empirical model. The proposed AMPSONN method is tested against BP, MPSO and PSOBP methods on an industrial corrosion dataset for different test conditions. The results showed that AMPSONN performs best on all three problems, exhibiting high classification accuracies and time efficiency.

show abstract

Medical image classification with convolutional neural network

Cited by 546 publications

References 22 publications

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Learning laparoscopic video shot classification for gynecological surgery

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

Contact Info

Product

Resources

About