Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Kingsley A. Ogudo, R. Surendran, Osamah Ibrahim Khalaf

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.

Original languageEnglish
Pages (from-to)693-707
Number of pages15
JournalComputer Systems Science and Engineering
Issue number1
Publication statusPublished - 2022


  • Deep learning
  • dermoscopic images
  • intelligent models
  • machine learning
  • skin lesion

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • General Computer Science


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