Journal: Scientific Reports

Article Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

doi: 10.1038/s41598-024-72237-x

Figure Lengend Snippet: Review of existing leaf disease methodologies with limitations.

Article Snippet: Da Rocha et al. , BO DL , AlexNet, ResNet50, SqueezeNet , Lack of extensive hyperparameter optimization.

Techniques: Extraction, Modification

Journal: Scientific Reports

Article Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

doi: 10.1038/s41598-024-72237-x

Figure Lengend Snippet: Comparison of the proposed approach with the latest approaches (tomato leaf 10 classes).

Article Snippet: Da Rocha et al. , BO DL , AlexNet, ResNet50, SqueezeNet , Lack of extensive hyperparameter optimization.

Techniques: Comparison, Modification

Journal: Scientific Reports

Article Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

doi: 10.1038/s41598-024-72237-x

Figure Lengend Snippet: Comparison of the suggested approach with recently established models for various crops.

Article Snippet: Da Rocha et al. , BO DL , AlexNet, ResNet50, SqueezeNet , Lack of extensive hyperparameter optimization.

Techniques: Comparison

Journal: Scientific Reports

Article Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

doi: 10.1038/s41598-024-72237-x

Figure Lengend Snippet: Comparison of the proposed model's training parameters with state-of-the-art models.

Article Snippet: Da Rocha et al. , BO DL , AlexNet, ResNet50, SqueezeNet , Lack of extensive hyperparameter optimization.

Techniques: Comparison

Journal: Diagnostics

Article Title: MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

doi: 10.3390/diagnostics11020359

Figure Lengend Snippet: A list of related studies that used histopathological images along with their limitations.

Article Snippet: [ ] , N/A , AlexNet , Softmax , 79.3% , Classic Desmoplastic Nodular Large Cell (anaplastic) , Depends only on spatial deep learning (DL) features..

Techniques: Extraction, Biomarker Discovery

Journal: Diagnostics

Article Title: MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

doi: 10.3390/diagnostics11020359

Figure Lengend Snippet: A comparison between MB-AI-His and related CADx based on the same dataset.

Article Snippet: [ ] , N/A , AlexNet , Softmax , 79.3% , Classic Desmoplastic Nodular Large Cell (anaplastic) , Depends only on spatial deep learning (DL) features..

Techniques: Comparison

Journal: Scientific Reports

Article Title: Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

doi: 10.1038/srep20410

Figure Lengend Snippet: The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in AlexNet ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.

Article Snippet: AlexNet , Softmax , 0.834.

Techniques: Biomarker Discovery

Journal: Scientific Reports

Article Title: Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

doi: 10.1038/srep20410

Figure Lengend Snippet: Comparison of DCNNs with other methods on the same dataset.

Article Snippet: AlexNet , Softmax , 0.834.

Techniques: Comparison

Journal: Sensors (Basel, Switzerland)

Article Title: ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection †

doi: 10.3390/s21113883

Figure Lengend Snippet: Summary of related work.

Article Snippet: 2017, Mansour et al. [ ] , AlexNet with multiple optimization techniques , Accuracy of 95.26% with principal component analysis and 97.93% with FC7 features , EyePACS.

Techniques: Biomarker Discovery, Activation Assay

Journal: Sensors (Basel, Switzerland)

Article Title: ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection †

doi: 10.3390/s21113883

Figure Lengend Snippet: Summary of related work.

Article Snippet: 2018, Wang et al. [ ] , AlexNet, VGG16, and Inception-V3 , 37.43%, 50.03%, 63.23% accuracy, respectively , EyePACS.

Techniques: Biomarker Discovery, Activation Assay