Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm

• Published in: Network (Bristol) Vol. 36; no. 3; p. 855
• Main Authors: Devadhas, David Neels Ponkumar, Isaac Sugirtharaj, Hephzi Punithavathi, Fernandez, Mary Harin, Periyasamy, Duraipandy
• Format: Journal Article
• Language: English
• Published: England 03.07.2025
• Subjects: Algorithms; Deep Learning; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Neural Networks, Computer; Skin Neoplasms - diagnosis; Skin Neoplasms - diagnostic imaging; Deep Neural Network (DNN); ensemble learning; Local Binary Patterns (LBP); Fuzzy-C-means clustering; Convolutional Neural Network (CNN)
• ISSN: 1361-6536
• DOI: 10.1080/0954898X.2024.2346608

Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the lesions. To tackle these problems, the proposed model develops an ensemble of deep learning techniques for skin cancer diagnosis. Initially, skin imaging data are collected and preprocessed using resizing and anisotropic diffusion to enhance the quality of the image. Preprocessed images are fed into the Fuzzy-C-Means clustering technique to segment the region of diseases. Stacking-based ensemble deep learning approach is used for classification and the LSTM acts as a meta-classifier. Deep Neural Network (DNN) and Convolutional Neural Network (CNN) are used as input for LSTM. This segmented images are utilized to be input into the CNN, and the local binary pattern (LBP) technique is employed to extract DNN features from the segments of the image. The output from these two classifiers will be fed into the LSTM Meta classifier. This LSTM classifies the input data and predicts the skin cancer disease. The proposed approach had a greater accuracy of 97%. Hence, the developed model accurately predicts skin cancer disease.