Comparison of the classification performance of S²MMAM and two other supervised medical image classification models. by Yuting Xue (8181303)

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Classification results for seed. by Xu Yang (112496)

“…The experimental results in ResNet34 show that the classification accuracy of the dataset before and after RGB reconstruction increases from 88.87% to 91.75%, demonstrating that RGB image reconstruction can strengthen image features; ResNet18, ResNet34, ResNet50, ResNet101, CBAM-ResNet34, SENet-ResNet34, and SENet-ResNet34-DCN models have classification accuracies of 72.25%, 91.75%, 89%, 88.48%, 92.28%, 92.80%, and 94.24%, respectively.SENet-ResNet34-DCN achieves the greatest classification accuracy results, with a model loss of roughly 0.3. …”

GM Mask image count after preprocessing. by Muhammad Zubair (728141)

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Classification accuracy of tree species with different models (%). by Jian Wang (5901)

Classification results of six heterogeneities using different network models. by Fulong Liu (22614787)

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Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts. by Gillian P. McHugo (8965919)

“…<p>Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts.…”

Image confusion matrix results of two models. by Sheng Li (107261)

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IDC dataset performance comparison with various existing models. by Muhammad Haris Abid (16469824)

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Performance of the five models on tissue classification inside ulcerations. by Che Wei Chang (12101782)

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Tissue classification from models with different labeling methods. by Che Wei Chang (12101782)

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Accuracy obtained by existing models and models used in the study. by Rubina Sarki (11985584)

The effect of increasing/decreasing confederates’ confidence on participants’ responses. by Winny W. Y. Yue (11146428)

Involvement of POm increases as discriminability of texture stimuli decreases. by Jia Qi (400499)

List of 421 genes whose expression is increased by galactose and decreased by any of the HAT inhibitors. by Masaya Nagaya (13851123)

List of 412 genes whose expression is decreased by galactose and increased by any of the HAT inhibitors. by Masaya Nagaya (13851123)

Join-point regression analysis of the statistically significant linear increase in use of antidepressants during the observed time period. by Jelena Filimonovic (22305307)

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CLASSIFICATION OF Phaseolus lunatus L. USING IMAGE ANALYSIS AND MACHINE LEARNING MODELS by ÉRIKA BEATRIZ DE LIMA CASTRO (14119929)

“…<div><p>ABSTRACT Image analysis combined with machine learning models can be an excellent tool for classification of fava (Phaseolus lunatus L.) genotypes and is a low-cost system. …”

Performance analysis of pretrained convolutional neural network models for ophthalmological disease classification by Busra Emir (9930280)

“…<div><p>ABSTRACT Purpose: This study aimed to evaluate the classification performance of pretrained convolutional neural network models or architectures using fundus image dataset containing eight disease labels. …”

ISIC2018 dataset performance comparison with various existing models. by Muhammad Haris Abid (16469824)

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Comparison of the classification performance of S²MMAM and five other semi-supervised medical image classification models. by Yuting Xue (8181303)

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