Title: Skin cancer classification using ensemble classification model with improved deep joint segmentation

Authors: Jinu P. Sainudeen; S. Sathyalakshmi

Addresses: Department of Information Technology, Hindustan Institute of Technology and Science, Chennai, Rajiv Gandhi Salai (OMR), Padur, Kelambakkam, Tamil Nadu 603103, India ' Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Chennai, Rajiv Gandhi Salai (OMR), Padur, Kelambakkam, Tamil Nadu 603103, India

Abstract: We present a six-phase skin cancer classification model based on improved deep joint segmentation (IDJS) in this work. The pre-processed image is segmented using IDJS in the second phase, after contrast enhancement with assistance from contrast limited adaptive histogram equalisation (CLAHE) in the first phase. The features of GLCM, CCF, LGIP, and median ternary pattern (MTP) are retrieved in the third phase. Data augmentation for the extracted features is carried out in the fourth phase. The fifth phase is ensemble classification using the deep maxout, LSTM, and CNN based on the enhanced data. To determine the final classified label, the enhanced score level fusion receives the output scores from these classifiers. While the RF is 0.9171, Deep Maxout is 0.9382, LSTM is 0.9362, Bi-GRU is 0.8150, RNN is 0.8687, CNN is 0.9382, TL-GOOGLENET is 0.9134, and KNN is 0.9328, respectively, the accuracy of the Ensemble approach is 0.9689.

Keywords: DL; skin cancer; segmentation; classification; recommendation.

DOI: 10.1504/IJBRA.2025.144027

International Journal of Bioinformatics Research and Applications, 2025 Vol.21 No.1, pp.72 - 101

Received: 20 Dec 2023
Accepted: 11 Mar 2024
Published online: 21 Jan 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article