EffSVMNet: An efficient hybrid neural network for improved skin disease classification

The Human Body’s primary defense layer is the skin which protects important organs from various external assaults. This organ protects our internal systems, safeguarding them from possible injury caused by viruses, fungus, and other factors. Unfortunately, the skin is not impenetrable, and infections or damage can occur, which leads to serious problems of health. Even a little skin lesion has the power to become a huge issue. As a result, in our study, our target is to produce an effective system for the quick and early identification of skin illnesses using well-known Convolutional Neural Networks (CNNs). The idea is to use this specialized neural network architecture to improve and speed up the detection and classification process to reduce time-lagging for treatment options. The proposed model i.e., EffSVMNet is a hybrid model consisting of a CNN classifier similar to EfficientNet B3 architecture coupled with a support vector machine (SVM). The sample dataset containing four classes i.e., acne, atopic dermatitis, bullous disease, and eczema is a subset of the DermNet dataset. The proposed model is not only lightweight but also achieves better validation accuracy when compared to similar methods in its category.