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

[Display omitted] •To design a novel skin disease classification model using enhanced deep learning approach with the help of hybrid optimization algorithm for enhancing accurate classification of skin diseases at the early stage for providing the earlier treatment for the affected individuals.•To implement the ensemble of weighted deep features by concatenating the extracted features from Deeplabv3, Resnet50 and Vgg16 along with the integration of optimal weights to each feature using proposed Hybrid Squirrel Butterfly Search Optimization (HSBSO).•To develop a Modified Long Short Term Memory (MLSTM) classifier using the developed HSBSO by optimizing certain parameters in LSTM for maximizing the accuracy of the skin disease classification and to detect various type of skin disease through the proposed model.•To introduce a novel algorithm named HSBSO for tuning the integrated weights of the deep features for reducing the training complexity in classification and also achieves the accurate classification performance by optimizing the hidden neurons of LSTM for improving the overall efficiency of the proposed skin disease classification model.•To validate the effectiveness of the suggested skin disease classification model based on two standard datasets by comparing with the conventional meta-heuristic algorithms and existing classifiers with various quantitative measures. Skin diseases are considered to be a common disease in human, which have many invisible dangers that may reduce the self-confidence and causes certain psychological depression and in-depth, these skin diseases may also results in skin cancer. These skin diseases needs to be diagnosed with the medical experts but it requires high-level instruments for diagnosing as they suffer from the visual resolution problem while analyzing the skin disease images. Hence, it is important to implement a computer-aided detection scheme for automatically diagnosing the skin disorder. Hence, this work plans to implement an effective skin disease classification method with the help of novel deep learning methodology. Initially, the dataset is gathered and pre-processed by the contrast enhancement technique through “histogram equalization”. After pre-processing, the segmentation of the images is done by the Fuzzy C Means segmentation (FCM). Further the segmented images assigned as a input for the deep feature extraction using Resnet50, VGG16, and Deeplabv3. The features are attained from the final layer of the