WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

Abstract

Skin cancer (SC) is the most prevalent cancer globally. Clinical assessment of skin lesions is needed to assess the disease characteristics; but, it is varied in interpretation and limited by longer timelines. Dermoscopy refers to a non-invasive imaging method that permits dermatologists to examine skin lesions with improved visualization of surface and subsurface structures. Machine-learning (ML) and deep learning (DL) methods were established to support dermatologists and overcome the problem as accurate and earlier diagnoses of SC is critical to increasing survival rate of the patient. In recent years, SC classification on dermoscopy images using DL has received great deal of interest. DL algorithms, especially convolutional neural networks (CNNs), have proved promising outcomes in precisely classifying skin lesions and differentiating between benign and malignant cases. The study presents a new Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification (DBOA-MMDLSCC) technique on dermoscopic images. The presented DBOA-MMDLSCC technique exploits the ensemble learning mechanism by the use of three DL models with metaheuristic based hyperparameter tuning. In the presented DBOA-MMDLSCC technique, U-Net++ model is utilized for skin lesion segmentation. For feature extraction, multi-modal DL model comprising three DL models namely Xception, Residual Network (ResNet), and SqueezeNet. Moreover, the hyperparameter tuning of the DL approaches takes place using the DBOA. Lastly, convolutional autoencoder (CAE) model is applied for detecting and classifying SC. A wide range of experiments were performed to exhibit the simulation results of the DBOA-MMDLSCC technique. The experimental values highlighted the improved performance of the DBOA-MMDLSCC technique in terms of different evaluation measures.

Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction

“…The proposed method leverages these techniques to classify skin lesions as benign or malignant with an accuracy of 93.18%. Srilakshmi Ch et al [43] propose a new method for the analysis of dermoscopic images called DBOA-MMDLSCC. The DBOA-MMDLSCC technique utilizes ensemble learning, combining three DL models with hyperparameter tuning using a metaheuristic approach.…”