Classification of skin disease using a novel hybrid flash butterfly optimization from dermoscopic images

Abstract

The failure of skin disease detection at an early stage leads to causes of well-known cancer known as melanoma, and it is created due to an assortment of dermatological conditions. Based on morphological attributes, design, surface, and shading, they are isolated into different classifications. To minimize the mortality rate, the early and timely prediction and diagnosis model is essential in medical field; so, to perform automatic detection, a novel hybrid flash butterfly optimized convolutional neural network with bidirectional long short-term memory (HFB-CNN-BiLSTM) approach is to accurately predict and classify the category of skin disease captured from dermoscopic images. The images are gathered from Ham10000 datasets that are highly imbalanced, and during training, it degrades classification performance. Therefore, the images are balanced by using preprocessing pipeline like augmentation by increasing the number of training samples to improve the efficiency of classification performance. Then feature extraction and classification processes are performed using HFB-CNN-BiLSTM to extract the relevant image features and classify them accurately based on their lesion characteristics as normal and abnormal (melanoma, benign keratosis, and melanocytic nevus). Moreover, the proposed framework’s viability is examined using MATLAB2018b software, and the performance is validated by comparison with existing approaches for various metrics. As a result, the proposed HFB-CNN-BiLSTM approach is highly superior in terms of all performance metrics compared to other existing approaches. The classification accuracy achieved by the proposed HFB-CNN-BiLSTM model in detecting three kinds of skin diseases is about 96.3%.