Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network

Arch Dermatol Res. 2024 May 25;316(6):275. doi: 10.1007/s00403-024-03076-z.


Purpose: A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful.

Methods: This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset.

Results: As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features.

Conclusion: Therefore, two stage prediction model achieved better results with feature fusion.

Keywords: ABCD; Benign; Convolutional neural network; Feature fusion; Feature selection; Lesion; Malignant; Melanoma.

MeSH terms

  • Deep Learning
  • Dermoscopy / methods
  • Humans
  • Image Interpretation, Computer-Assisted / methods
  • Machine Learning
  • Melanoma* / diagnosis
  • Melanoma* / pathology
  • Melanoma, Cutaneous Malignant
  • Neural Networks, Computer*
  • Skin / diagnostic imaging
  • Skin / pathology
  • Skin Neoplasms* / diagnosis
  • Skin Neoplasms* / pathology