Journal: Frontiers in Big Data

Article Title: MARGIN: Uncovering Deep Neural Networks Using Graph Signal Analysis

doi: 10.3389/fdata.2021.589417

Figure Lengend Snippet: Our approach identifies the most salient regions in different classes for image classification using AlexNet. From top to bottom: original image, MARGIN’s explanation overlaid on the image, and Grad-CAM’s explanation. Note our approach yields highly specific, and sparse explanations from different regions in the image for a given class.

Article Snippet: For Kaggle, we use penultimate features from AlexNet in order to construct a neighborhood graph.

Techniques:

Journal: Ipem-Translation

Article Title: Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

doi: 10.1016/j.ipemt.2022.100008

Figure Lengend Snippet: AlexNet Architecture used for COVID-19 classification .

Article Snippet: , X-ray , Combination of Two Different DBs , 105 COVID-19, 11 SARS, 80 Normal Img. , 70%:30% , Hold-out , ImageNet , Supervised , Data Augmentation, Histogram, Feature Extraction using AlexNet, PCA, K-means , COVID-19 from Other Pneumonia , NA , DeTraC (Based on ResNet18), AlexNet (Feature Extraction) , Softmax , Composition Phase , Acc=95.12, Sen=97.91, Spe=91.87.

Techniques:

Journal: Ipem-Translation

Article Title: Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

doi: 10.1016/j.ipemt.2022.100008

Figure Lengend Snippet: A combined architecture of AlexNet, SqueezeNet, GoogleNet, and MobileNetV2 for COVID-19 detection .

Article Snippet: , X-ray , Combination of Two Different DBs , 105 COVID-19, 11 SARS, 80 Normal Img. , 70%:30% , Hold-out , ImageNet , Supervised , Data Augmentation, Histogram, Feature Extraction using AlexNet, PCA, K-means , COVID-19 from Other Pneumonia , NA , DeTraC (Based on ResNet18), AlexNet (Feature Extraction) , Softmax , Composition Phase , Acc=95.12, Sen=97.91, Spe=91.87.

Techniques:

Journal: Ipem-Translation

Article Title: Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

doi: 10.1016/j.ipemt.2022.100008

Figure Lengend Snippet: Summary of recent DL techniques for COVID-19 diagnosis.

Article Snippet: , X-ray , Combination of Two Different DBs , 105 COVID-19, 11 SARS, 80 Normal Img. , 70%:30% , Hold-out , ImageNet , Supervised , Data Augmentation, Histogram, Feature Extraction using AlexNet, PCA, K-means , COVID-19 from Other Pneumonia , NA , DeTraC (Based on ResNet18), AlexNet (Feature Extraction) , Softmax , Composition Phase , Acc=95.12, Sen=97.91, Spe=91.87.

Techniques: Biomarker Discovery, Extraction, Activation Assay, Bacteria, Preserving, Shear, Infection, Sampling, Blocking Assay

Journal: Frontiers in Human Neuroscience

Article Title: A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

doi: 10.3389/fnhum.2024.1347082

Figure Lengend Snippet: Studies using Deep Learning methods for detection of SCZ.

Article Snippet: , Short Term FFT, Continuous WT, and SPWVD , AlexNet, ResNet50, VGG16, and CNN , EEG , Kaggle SCZ dataset ( ) , 93.36.

Techniques:

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: Scientific Reports

Article Title: Chinese herbal medicine recognition network based on knowledge distillation and cross-attention

doi: 10.1038/s41598-025-85697-6

Figure Lengend Snippet: Performance comparison of different models.

Article Snippet: In 2020, Huang et al. built a dataset containing five classes of Chinese herbal medicines by crawling Baidu images and, based on this dataset, proposed a Chinese herbal medicine image classification method based on the AlexNet model. Hu et al. drew on the concept of multi-task learning, using neural networks as the foundation and traditional features as a supplement, integrating the two to establish a new deep learning model, and collected 200 classes of Chinese herbal slices for Chinese herbal slice recognition.

Techniques: Comparison

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: Lam et al. [ ] , Kaggle MESSIDOR-1 , , Pretrained AlexNet and GoogLeNet , GoogLeNet 2-ary, 3-ary and 4-ary , 2-ary accuracy 74.5% 3-ary accuracy 68.75% 4-ary accuracy 51.25%.

Techniques: Biomarker Discovery

Journal: Computational Intelligence and Neuroscience

Article Title: Acceleration of Deep Neural Network Training Using Field Programmable Gate Arrays

doi: 10.1155/2022/8387364

Figure Lengend Snippet: The accuracy comparison for AlexNet model.

Article Snippet: It achieves 203.75 GOPS on Terasic DE1 SoC with the AlexNet model and 196.50 GOPS with the VGG-16 model on Terasic DE-SoC.

Techniques: Comparison