Automatic eczema classification in clinical images based on hybrid deep neural network.

The healthcare sector is the highest priority sector, and people demand the highest services and care. The fast rise of deep learning, particularly in clinical decision support tools, has provided exciting solutions primarily in medical imaging. In the past, ANNs (artificial neural networks) have been used extensively in dermatology and have shown promising results for detecting various skin diseases. Eczema represents a group of skin conditions characterized by irritated, dry, inflamed, and itchy skin. This study extends great help to automate the diagnosis process of various kinds of eczema through a Hybrid model that uses concatenated ReliefF optimized handcrafted and deep activated features and a support vector machine for classification. Deep learning models and standard image processing techniques have been used to classify eczema from images automatically. This work contributes to the first multiclass image dataset, namely EIR (Eczema image resource). The EIR dataset consists of 2039 labeled eczema images belonging to seven categories. We performed a comparative analysis of multiple ensemble models, attention mechanisms, and data augmentation techniques for this task. The respective accuracy, sensitivity, and specificity, for eczema classification by classifiers were recorded. In comparison, the proposed Hybrid 6 network achieved the highest accuracy of 88.29%, sensitivity of 85.19%, and specificity of 90.33%% among all employed models. Our findings suggest that deep learning models can classify eczema with high accuracy, and their performance is comparable to dermatologists. However, many factors have been elucidated that contribute to reducing accuracy and potential scope for improvement.