Distinguishing natural and computer generated images using Multi-Colorspace fused EfficientNet

Gangan, Manjary P.; Anoop, K; L, Lajish V.

doi:10.1016/j.jisa.2022.103261

Cited by 14 publications

(2 citation statements)

References 43 publications

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“…There are many pre-trained models of CNN available and they are being widely used in the field of image processing, such as LeNet, AlexNet, ResNet, GoogleNet or InceptionNet, VGG, DenseNet, EfficientNet, PolyNet, and many more. CNN is basically originated from neural network with convolution layers, pooling layers, activation layers, etc., and those mentioned pre-trained networks are specific CNNs designed for various applications, such as classification and localization [ 2 , 12 , 13 , 14 , 15 , 16 , 17 , 31 , 32 , 33 , 34 , 37 ].…”

Section: Methodologiesmentioning

confidence: 99%

“…In addition to this, authors also attempted to resolve the data imbalance problem by introducing fine-tuned global attention block and category attention block to obtain more detailed information of small lesions. Manjary P et al [ 33 ] proposed a classification model to distinguish between natural and computer-generated images by designing a multi-color-space fused EfficientNet using transfer learning methodology which operates in three different color spaces. Ying Guo et al [ 34 ] proposed an EfficientNet based multi view feature fusion model for cervical cancer screening.…”

Section: Literature Surveymentioning

confidence: 99%

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Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

Mohanty

Pradhan

Reddy³

et al. 2022

Cancers

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This study mainly focuses on pre-processing the HAM10000 and BCN20000 skin lesion datasets to select important features that will drive for proper skin cancer classification. In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). Then, two other strategies, Model-based Optimized Weighted Feature Set (MOWFS) and Feature-based Optimized Weighted Feature Set (FOWFS), are proposed by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish (AJS) algorithm. The MOWFS-AJS is a model-specific approach whereas the FOWFS-AJS is a feature-specific approach for optimizing the weights chosen for obtaining optimal feature sets. The performances of those three proposed feature selection strategies are evaluated using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers and the performance are measured through accuracy, precision, sensitivity, and F1-score. Additionally, the area under the receiver operating characteristics curves (AUC-ROC) is plotted and it is observed that FOWFS-AJS shows the best accuracy performance based on the SVM with 94.05% and 94.90%, respectively, for HAM 10000 and BCN 20000 datasets. Finally, the experimental results are also analyzed using a non-parametric Friedman statistical test and the computational times are recorded; the results show that, out of those three proposed feature selection strategies, the FOWFS-AJS performs very well because its quick converging nature is inculcated with the help of AJS.

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