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An Efficient Galactic Swarm Optimization Based Fractal Neural Network Model with DWT for Malignant Melanoma Prediction

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Abstract

The most aggressive and malignant type of skin cancer is melanoma. When input data is in the form of images, image processing plays a vital role to detect and classifying cancer in the human body. Existing research discovered many weaknesses in complex data models, such as higher feature dimensionality, which required more data for training, resulting in lower detection accuracy, higher computational difficulties, portability, and processing time. Hence, we introduced fractal neural network-based galactic swarm optimization (FNN–GSO) algorithm for the detection of malignant melanoma such as superficial spreading, nodular, and lentigo malignant melanoma. The main aim of this work is to apply a deep learning technique to classify skin lesions for effective treatment and prognostication instead of the gold standard excision biopsy, which is currently used to diagnose this condition. Expert analysis, time consumption, and expensive processing associated with malignant melanoma classification and prediction are minimized in this manner. The proposed work involves four major components such as pre-processing, segmentation, feature extraction, and classification. The raw input images are pre-processed thereby the noise removal and contrast level enhancement are carried out. An adaptive watershed segmentation algorithm performs malignant melanoma segmentation. Following that, image features such as are extracted correctly using the DWT–GLCM feature extraction model. Finally, the malignant melanoma classifications are performed using the FNN–GSO algorithm. The proposed method's performance is evaluated using MATLAB software and different evaluation parameters. The proposed FNN–GSO algorithm demonstrates better classification results than other existing methods.

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Authors and Affiliations

  1. Department of MBA, St. Joseph’s College of Engineering, Chennai, India

    S. P. Karuppiah

  2. Department of CSE, St. Joseph’s Institute of Technology, Chennai, India

    Adlin Sheeba, S. Padmakala & C. A. Subasini

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  1. S. P. Karuppiah
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Karuppiah, S.P., Sheeba, A., Padmakala, S. et al. An Efficient Galactic Swarm Optimization Based Fractal Neural Network Model with DWT for Malignant Melanoma Prediction. Neural Process Lett 54, 5043–5062 (2022). https://doi.org/10.1007/s11063-022-10847-0

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