Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification

Joyseeree, Ranveer; Otálora, Sebastian; Müller, Henning; Depeursinge, Adrien

doi:10.1016/j.media.2019.06.006

Cited by 22 publications

(12 citation statements)

References 60 publications

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“…Similarly, a total of 147 images are taken for testing in which each class has 21 images. Also, the efficiency of these classifier models is compared with DNN [9], CADx [11], MLP [13], CNN [16], and MIL [17] in terms of precision, recall, fmeasure, and accuracy.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

Nagarajan¹,

Tajunisha²

2021

RIA

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The classification of ovarian cancer types is a very challenging process for physicians' eyes. To solve this problem, this article proposes a new deep learner, which classifies ovarian cancer types from Computerized Tomography (CT) images. Firstly, a Deep Convolutional Neural Network (DCNN) model depending on AlexNet is proposed to categorize ovarian cancer from CT images. But its efficiency is not satisfactorily high. So, DCNN is built based on the fusion of AlexNet, VGG, and GoogLeNet. The fusion is carried out at the SoftMax layer by fusing the SoftMax values of each network structure using a weighted sum to obtain the overall classification outcome. But overfitting problems can occur due to an inadequate number of training images. Thus, a Deep Semi-Supervised Generative Learning with DCNN model (DSSGL-DCNN) is proposed by using a Generative Adversarial Network (GAN) which augments the training samples to solve the overfitting problem. Once the augmented dataset is obtained, the fused DCNN model is learned to classify ovarian cancer types. Further, the classified outcomes can be used as a useful guideline for physicians in medical diagnosis. Finally, the experimental results show that the DSSGL-DCNN achieves higher efficiency compared to the other DCNN architectures.

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Section: Resultsmentioning

confidence: 99%

“…Joyseeree et al [11] suggested a new technique for classifying the different tags of infected and healthy pulmonary tissues from CT according to the concatenation of Riesz and deep features. Initially, discriminative parametric pulmonary tissue texture signs were trained from Riesz interpretations via a 1-vs.-1 method.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

Nagarajan¹,

Tajunisha²

2021

RIA

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“…The method of sampling applied to fix the imbalanced data. [7] proposed to classify the tissues in lung. It aimed to detect and perform classification on lung disease based on the tissue in the lung.…”

Section: Literature Reviewmentioning

confidence: 99%

Acute Myelogenous Leukemia Detection using Circumventing Ant Colony Optimization based Convolutional Neural Network

Ramya*,

Rani

2019

IJRTE

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Acute Myelogenous Leukemia (AML) is a type of disease associated with acute leukemia which is getting increased in both children’s and adults. AML falls under the category of cancer disease. The term acute in AML indicates rapid progression of disease in human body. The main challenge of medical field in vision of computer and multimedia is texture and color between various categories. The variation in texture and color attributes makes the classification task a tedious. Deep learning has shown its dazzling performance in various streams, which includes classification too. The objective of image classification is to differentiate the subcategories that belong to same basic-level category. The main objective of this paper is to propose bioinspired based on convolutional neural network to classify the microscopic blood images for AML. This paper has utilized bioinspired concept to extract the features more reliably. Bench mark performance metrics were chosen to evaluate the proposed classifier against the previous classifiers based on two parameters. The results indicate that the proposed classifiers has outperformed the previous works towards the classification of AML.

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“…and (c) "fine-tuning" that aims to gradually train layers by tuning the learning parameters until a significant performance boost is achieved. Transfer knowledge via fine-tuning scenario demonstrated outstanding performance in chest X-ray and computed tomography image classification [12], [13].…”

Section: Introductionmentioning

confidence: 99%

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection

Abbas

Abdelsamea

Gaber

2021

IEEE Trans. Neural Netw. Learning Syst.

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Due to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for Transfer learning (4S-DT) model. 4S-DT encourages a coarseto-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabelled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a classdecomposition layer to simplify the local structure of the data. 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream classdecomposition mechanism. We used 50,000 unlabelled chest Xray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. 4S-DT has achieved a high accuracy of 99.8% (95% CI: 99.44 %, 99.98%) in the detection of COVID-19 cases on a large dataset and an accuracy of 97.54% (95% CI: 96.22%, 98.91%) on an extended test set enriched by augmented images of a small dataset, out of which all real COVID-19 cases were detected, which was the highest accuracy obtained when compared to other methods.

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Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification

Cited by 22 publications

References 60 publications

Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

Acute Myelogenous Leukemia Detection using Circumventing Ant Colony Optimization based Convolutional Neural Network

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection

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