Journal: Multimedia Tools and Applications

Article Title: A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

doi: 10.1007/s11042-022-12642-4

Figure Lengend Snippet: Literature Review on ML and DL models for early detection of DR

Article Snippet: Gardner et al. [ ] , EyePACS-1 MESSIDOR-2 , HE and MA , Inception-V3-architecture Neural Network , Ensemble of 10 networks , EyePACS-1: AUC of 0.991 MESSIDOR-2: AUC of 0.990.

Techniques: Biomarker Discovery

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Results for pre-trained models using dataset #1.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Results for pre-trained-MCSVM based models using dataset #1.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Results for pre-trained models with image SR using dataset #1.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Results for pre-trained-MCSVM based models with image SR using dataset #1.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Results for pre-trained models with image SR using dataset #2.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Results for pre-trained-MCSVM-based models with image SR using dataset #2.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Results for pre-trained models with image SR using dataset #3.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Results for pre-trained MCSVM-based models with image SR using dataset #3.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: The best results for pre-trained models with image super-resolution using the three datasets.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Diagnostics

Article Title: Simultaneous Super-Resolution and Classification of Lung Disease Scans

doi: 10.3390/diagnostics13071319

Figure Lengend Snippet: Computational time of the examined approaches using dataset #1.

Article Snippet: , Inceptionv3 + Softmax , 93.85 , 92.64 , 96.86 , 92.20 , 90.02 , 92.56 , 0.0534.

Techniques:

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: The architecture of the Inception v3 model: base learner 1 (image has been made by R.K. using Google Slides).

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques:

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Mathematical steps of the proposed ensemble method using three CNN base models. I represents the input images; P represents the decision scores generated by the base learner and i represents the base learners: Inception v3 ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i=1$$\end{document} i = 1 ), Xception ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i=2$$\end{document} i = 2 ) and DenseNet-169 ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i=3$$\end{document} i = 3 ) (image has been made by R.K. using Google Slides).

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques: Generated

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Results obtained on ensembling various combinations of base learners on all the three datasets used in this study.

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques:

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques:

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Comparison of the classification performance of the base learners and their ensemble using the proposed scheme.

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques: Comparison

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Loss curves obtained on fine-tuning the three CNN base learners: Inception v3, Xception and DenseNet-169 on the three datasets used in this research— (a–c) SIPaKMeD 2-class dataset, (d–f) SIPaKMeD 5-class dataset and (g–i) Mendeley LBC 4-class dataset (The loss curves have been plotted using Keras framework of Python).

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques:

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Comparison of the proposed ensemble model with some standard CNN models in literature: Inception v3 , Xception , DenseNet-169 , ResNet-18 , VGG-19 (image has been made by R.K. using Google Sheets).

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques: Comparison

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Comparison of the proposed ensemble model with some popular fusion techniques in literature using the same base learners: Inception v3, Xception and DenseNet-169 (image has been made by R.K. using Google Sheets).

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques: Comparison

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Examples of test samples from the SIPaKMeD Pap Smear dataset where one or more of the base classifiers predict incorrectly, but the ensemble predicts correctly. (a) DenseNet-169 classifies the sample as: “Koilocytotic” with confidence 31%, Xception classifies the sample as: “Parabasal” with confidence 36% and Inception v3 classifies the sample as: “Metaplastic” with confidence 98%. Ensemble prediction is: “Metaplastic”. (b) DenseNet-169 classifies the sample as: “Koilocytotic” with confidence 32%, Xception classifies the sample as “Parabasal” with confidence 95%, and Inception v3 classifies the sample as “Parabasal” with confidence 98%. Ensemble prediction is: “Parabasal”.

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques:

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Results of the McNemar’s test performed between the proposed ensemble model and the base learners used: null hypothesis is rejected for all cases.

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques: Comparison

Journal: Scientific Reports

Article Title: A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

doi: 10.1038/s41598-021-93783-8

Figure Lengend Snippet: Results (accuracies in %) obtained by the proposed ensemble framework and its base classifiers on the Zenodo 5K breast histopathology dataset.

Article Snippet: Figure 12 Visualization of the convolution filters of the Inception v3 model on the Mendeley LBC dataset (the plots have been formed using Keras framework of Python).

Techniques: Histopathology