Enhancing Photovoltaic Module Fault Diagnosis with Unmanned Aerial Vehicles and Deep Learning-Based Image Analysis by J. Jerome Vasanth, S. Naveen Venkatesh, V. Sugumaran, Vetri Selvi Mahamuni

“…In the initial deep learning phase, the final fully connected layer of six pretrained networks, namely, DenseNet-201, VGG19, ResNet-50, GoogLeNet, VGG16, and AlexNet, was utilized to extract PVM image features. …”
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Utilizing Age-Adaptive Deep Learning Approaches for Detecting Inappropriate Video Content by Iftikhar Alam, Abdul Basit, Riaz Ahmad Ziar

“…It achieves an impressive accuracy rate of (96.89%) in a 5-fold cross-validation setup, outperforming NasNet (92.6%), EfficientNet-B7 (87.87%), GoogLeNet (85.1%), and VGG-19 (92.83%). Furthermore, in a single run, it maintains a consistent accuracy rate of 90%. …”
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Using microscopic imaging and ensemble deep learning to classify the provenance of archaeological ceramics by Qian Wang, Xuan Xiao, Zi Liu

“…The proposed model includes the following: the construction of a dataset for ancient ceramic microscopic images, image preprocessing methods based on Gamma correction and CLAHE equalization algorithms, extraction of image features based on three deep learning architectures—VGG-16, Inception-v3 and GoogLeNet, and optimal fusion. This latter is based on stochastic gradient descent (SGD) algorithm, which allows optimal fitting of the fusion model parameters by freezing and unfreezing model layers. …”
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An Enhanced Approach Using AGS Network for Skin Cancer Classification by Hwanyoung Lee, Seeun Cho, Jiyoon Song, Hoyoung Kim, Youjin Shin

“…It was evaluated using eight deep learning classifiers—GoogLeNet, DenseNet201, ResNet50, MobileNet V3, EfficientNet B0, ViT, EfficientNet V2, and Swin Transformers—on the HAM10000 dataset. …”
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Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays by Nourhan Zayed, Nahed Tawfik, Mervat M. A. Mahmoud, Ahmed Fawzy, Young-Im Cho, Mohamed S. Abdallah

“…The findings of the proposed method prove that the GoogLeNet network is the best fit due to its lower computational resource requirements, speed, and accuracy. …”
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Assessment of using transfer learning with different classifiers in hypodontia diagnosis by Tansel Uyar, Didem Sakaryalı Uyar

“…Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). …”
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Enhancing furcation involvement classification on panoramic radiographs with vision transformers by Xuan Zhang, Enting Guo, Xu Liu, Hong Zhao, Jie Yang, Wen Li, Wenlei Wu, Weibin Sun

“…This study developed and assessed a ViT model for classifying FI from panoramic radiographs, and compared its performance with traditional models, including Multi-Layer Perceptron (MLP), Visual Geometry Group (VGG)Net, and GoogLeNet. Results Among the evaluated models, the ViT model outperformed all others, achieving the highest precision (0.98), recall (0.92), and F1 score (0.95), along with the lowest cross-entropy loss (0.27) and the highest accuracy (92%). …”
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