A Deep Ensemble Model for Automated Multiclass Classification Using Dermoscopy Images

Abstract

In medical diagnosis, manual skin tumor treatment is time consuming and exclusive, it is important to create computerized analytic strategies that can accurately classify skin lesions of many stages. A completely automatic way to classify skin lesions of many categories has been presented. Automatic dissection of skin lesions and isolation are two major and related functions in the diagnosis of computer-assisted skin cancer. Even with their widespread use, deep learning models are typically only intended to execute a single task, neglecting the potential benefits of executing both functions simultaneously. The Bootstrapping Ensembles based Convolutional Neural Networks (BE-CNN) model is proposed in this paper for the separation of skin lesions simultaneously and for classification. A Compute-Intensive Segmentation Network (CI-SN), comprise this model (improved-SN). On one hand, Compute-Intensive Segmentation Network creates uneven lesion covers that serves as a pre-bootstrapping, allowing it to reliably find and classify skin lesions. Both division and arrangement networks, in this approach, mutually transmit assistance and experience each other in a bootstrapping manner. However, to deal with the challenges posed by class inequality and simple pixel inequality, a novel method in segmentation networks is proposed. On the ISIC-HAM 10000 datasets, the proposed BE-CNN model is evaluated and found that it achieves mean skin lesion classification accuracy of 93.8 percentile, which is higher than the function of the separation of skin lesions representing the modern condition and stages techniques. Proposed outcomes demonstrate that via preparing a bound together model to execute the two tasks in a non-stop bootstrapping strategy, it is feasible to work on the presentation of skin sore division and grouping simultaneously.