Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory

Skin Type Detection with Deep Learning: A Comparative Analysis

“…Through performance analysis, the proposed HSBSO provided 26.8% better security than DNN, 18.8% better than CNN, 4.77% better than SVM, and 56.33% higher than LSTM in classification performance under dataset 2. Therefore, it was concluded that the proposed skin disease classification model using the proposed HSBSO and the development of the designed MLSTM outperform conventional skin disease classification models [25].…”

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

“…The concept of wider, deeper, and higher resolution properties of those pre-trained networks giving the network with more filters, more convolution layers and the ability to process the images with larger depth has gained popularity in the field of image processing. Considering those general advantages as well as a few other advantages, such as VGG16 is good at image classification, the effectiveness of model scaling, the proper use of baseline network in EfficientNet B0, and the principle of ResNet50 to build deeper networks and efficiency to obtain number of optimized layers to overcome the vanishing gradient problem, has been the motivation behind this work to design a deep feature fusion strategy for feature selection leading to an effective skin lesion image classification [ 2 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 17 , 18 , 19 , 20 ].…”

“…Similarly, Nils Gessert et al [ 41 ] also proposed an ensemble-based classification model for ISIC 2017 skin lesion classification challenge using EfficientNets, SENet, and ResNeXt WSL. Mohamed A. Elashiri et al [ 19 ] proposed an ensemble-based classification model with the weighted deep concatenated features with long short-term memory. These ensembles of weighted features are basically concatenated features from three CNNs pre-trained models, namely DeepLabv3, ResNet50, and VGG16 integrating the optimal weights of each feature using their proposed hybrid squirrel butterfly search algorithm.…”

“…Feature fusion, or in other words, the combination of features from different networks, is an omnipresent part of the model learning mechanisms, which is achieved in many ways. The simplest form is the concatenation of outputs of participating networks or using some means or methods of optimizing the weights of the opinions of the participating networks to obtain a good fusion of features having relative discriminative power to design a classification model [ 18 , 19 , 20 ]. The importance of using optimization in feature fusion is not only to just rank the ranking of features to obtain an optimized version of features, but also the optimized weights help to decide the impact of each feature even if a feature of first rank will have some weighted importance.…”

“…Our prime contributions in this research are: the transfer learning strategy was exploited with the help of CNN’s pre-trained networks for feature selection and feature fusion [ 2 , 16 , 17 , 18 ]; the advantages of visual geometry group network (VGG16), EfficientNet B0, and residual neural network (ResNet50) such as low number of parameters and small size filters, multi objective neural architecture optimizing the accuracy and floating point operations with a balanced depth, width, and resolution producing a scalable, accurate and easily deployable model; and the ability to solve the problem of vanishing gradients of those three pre-trained networks have been explored deeply while designing this deep feature fusion model [ 12 , 13 , 14 , 15 ]. The key advantages of the ensemble learning mechanism to design a robust feature selection model by proposing combined feature fusion strategies [ 19 , 20 , 21 ], such as combined feature set (CFS), adaptive weighted feature set (AWFS), model-based optimized weighted feature set (MOWFS), and feature-based optimized weighted feature set (FOWFS), are experimented and validated. In order to reduce the losses and selection of optimized weights of those three pre-trained networks, the advantages of a new meta-heuristic optimizer artificial jellyfish optimizer (AJS) [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] was used and finally, the performance of the proposed feature fusion strategies are likened to other combinations of the models with genetic algorithm (GA) [ 30 ] and particle swarm optimization (PSO) [ 31 ] such as MOWFA-GA, MOWFS-PSO, FOWFS-GA, and FOWPS-PSO, and it was observed that the proposed combination of FOWFS-AJS outperforms the other models used for classification of skin lesion diagnosis.…”