Enhanced U-Net segmentation with ensemble convolutional neural network for automated skin disease classification

In recent years, skin-related problems induce psychological problems and also injure physical health, particularly if the patient’s face was disfigured or damaged. Smart devices are used for gathering medical images for knowing their skin condition. Skin disease diagnosis is a complex task, which can be solved by adopting different lesion detection and classification approaches. However, the existing challenges cannot be solved by mixing the disease samples from diverse data sources while using simple data fusion approaches. The traditional deep learning-based computer-aided diagnosis approaches suffer from poor extraction of skin lesions due to complex features like limited training datasets, low contrast with the background, presence of artifacts, and fuzzy boundaries. It also includes problems like complex computation, poor generalization, and over-fitting while using the appropriate tuning of large-scale parameters. This paper intends to propose a new framework by using skin lesions classification and segmentation procedures for the automated diagnosis of various skin diseases. The significant stages of the given offered method are pre-processing lesion segmentation and classification. In the beginning, grey-level conversion, hair removal, and contrast enhancement are performed to make the image fit for effective classification. Once image pre-processing is over, the segmentation of skin lesions is done by the enhanced U-Net segmentation, in which the improvement is attained by proposing a hybrid optimization algorithm. Moreover, the offered hybridized optimization algorithm solves the local optimum issues, and also it has the ability for resolving a finite set of problems. Merging the optimization algorithms can balance the exploration and exploitation capability owing to its ability of convergence speed, searching global optimum, and simplicity. The classification is further performed by the optimized ensemble-convolutional neural network (E-CNN). Instead of the fully connected layer in CNN, five different expert systems like random forest, artificial neural network, support vector machine, Adaboost, and Extreme Gradient Boosting (XGBoost) are used for classifying the skin disease by CNN. The system also employs optimization of different parameters in the classification stage to improve computing efficiency and reduce network complexity. The hybrid meta-heuristic termed whale-electric fish optimization (W-EFO) based on EFO and whale optimization algorithm is used for improvising the segmentation and classification task. The comparative analysis over conventional models proves that the developed model encourages effective performance when analyzing diverse measures.