Many people suffer due to insect bites every year and it may even be life-threatening sometimes. Insect bites and stings come in different shapes and sizes and itchy red colors, and it might be a difficult task to accurately identify which bite is caused by which insect. On different skin, it may be in different colors or textures. This task may be even challenging for researchers and doctors. Analysis of insect bites is crucial for treatment and disease prevention. Deep learning (DL) algorithms have demonstrated promising gains in a variety of image identification applications in recent years. To enable automatic identification and classification of insect species, this study explores the use of DL algorithms for multi-class classification of insect bites. The main purpose of the paper is to propose a model that classifies insect bites on human skin using deep learning algorithms aiming to classify the insect bites more accurately. This paper first tried to classify whether the given image is a skin or any type of object and received an accuracy of 95% using pre-trained inception-v3 and for insect bite classification a proposed VINMOBCONCAT (hybrid model) received an accuracy of 93%.