Journal: Diagnostics

Article Title: A Novel Diagnostic Framework with an Optimized Ensemble of Vision Transformers and Convolutional Neural Networks for Enhanced Alzheimer’s Disease Detection in Medical Imaging

doi: 10.3390/diagnostics15060789

Figure Lengend Snippet: Summary of Related Works.

Article Snippet: Saim et al. (2024) [ ] , Nine CNN architectures (AlexNet, Inception, EfficientNet-b0, etc.) , Kaggle Alzheimer’s dataset ADNI dataset , 5091 Images (Train) 1273 Images (Test) , Feature extraction using pretrained CNNs (fully connected, inception-based, residual-based, etc.); classification using MSVM, KNN, DT; , Kaggle: ADNI: 78.54% (MSVM) , Early detection and staging of AD using neuroimaging biomarkers , Performance on ADNI dataset lower; needs further validation on larger and more diverse datasets..

Techniques: Biomarker Discovery, Imaging

Journal: Diagnostics

Article Title: A Novel Diagnostic Framework with an Optimized Ensemble of Vision Transformers and Convolutional Neural Networks for Enhanced Alzheimer’s Disease Detection in Medical Imaging

doi: 10.3390/diagnostics15060789

Figure Lengend Snippet: Comprehensive Comparison with Other Existing Studies.

Article Snippet: Saim et al. (2024) [ ] , Nine CNN architectures (AlexNet, Inception, EfficientNet-b0, etc.) , Kaggle Alzheimer’s dataset ADNI dataset , 5091 Images (Train) 1273 Images (Test) , Feature extraction using pretrained CNNs (fully connected, inception-based, residual-based, etc.); classification using MSVM, KNN, DT; , Kaggle: ADNI: 78.54% (MSVM) , Early detection and staging of AD using neuroimaging biomarkers , Performance on ADNI dataset lower; needs further validation on larger and more diverse datasets..

Techniques: Comparison, Stripping Membranes, Extraction, Biomarker Discovery, Imaging

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: Lastly ( ) apply STFT, CWT, and SPWVD with various CNN architectures (AlexNet, ResNet50, VGG16), achieving an accuracy of 93.36% on the Kaggle SCZ dataset. achieved 99.22% accuracy using Fourier Transform with CNN + LSTM, while ( ) reported 98% with Random Projection and RNN-LSTM. combined a Digital Bandpass Filter with a CNN + ML hybrid, reaching 98.05% accuracy.

Techniques:

Journal: Frontiers in Plant Science

Article Title: Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

doi: 10.3389/fpls.2023.1252166

Figure Lengend Snippet: Automation and AI Tools in plant monitoring.

Article Snippet: Alexnet , CNN-Long-Short Term Memories (LSTM) architecture , Canon EOS 650D , Plant growth pattern of different genotypes , A. thaliana , .

Techniques: Imaging, High Throughput Screening Assay, Software, Mutagenesis

Journal: Diagnostics

Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review

doi: 10.3390/diagnostics14080806

Figure Lengend Snippet: Study characteristics and accuracy results of the included studies.

Article Snippet: Hsiao et al., 2023, USA [ ] , - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 , - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image , PA , January 2011 to January 2019 , N = 788 , Implant Brands: N = 3 (A) BioHorizons (22.84%) (B) ST (34.51%) (C) NB (42.63%) Implant Models (A) BioHorizons: (1) Legacy implant Tapered Pro; (B) ST: (1) Bone Level, Bone Level Tapered, Standard Straumann, Tapered Effect; (C) NB: (1) Active, (2) Parallel, (3) Replace, (4) Replace Select Straight, (5) Replace Select Tapered, (6) Speedy Groovy, (7) Speedy Replace , EORS , Training Group: 75% Test Group: 25% , Overall implant-identification Accuracy: >90% Test accuracy (1) MnasNet6: 81.89% (2) ShuffleNet7: 96.85% (3) MobileNet8: 92.68% (4) AlexNet9: 94.35% (5) VGG10: 92.94% (6) ResNet11: 96.43% (7) DenseNet12: 96.41% (8) SqueezeNet13: 91.55% (9) ResNeXt14: 93.90% (10) Wide ResNet15: 92.01% , Tested CNN has high accuracy and speed.

Techniques: Indirect Immunoperoxidase Assay, Biomarker Discovery, Generated, Plasmid Preparation, Diagnostic Assay