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Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

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Abstract

Breast cancer (BC) has been one of the significant causes of death worldwide, and its early detection can play a vital role in increasing the survival rate of this disease. This paper suggests a novel feature learning method for BC diagnosis using the artificial neural network (ANN) by optimizing the hidden layers. It uses a systematic hyper-parameter search method to optimize number of hidden layers, neurons in the hidden layers, activation functions, learning rate, batch size, and training epochs of the ANN. The number of hidden layers and the number of neurons in the hidden layer are investigated using t-distributed stochastic neighbor embedding (t-SNE) technique to specify the best hyper-parameters that achieved the highest testing accuracy while avoiding model overfitting. Comparative analysis showed that the proposed method achieved higher performance than the ANN-based models that utilizes particle swarm optimization (PSO), multi-verse optimizer (MVO), BAT algorithm, and firefly algorithm (FFA) as feature selection (FS) methods, with the best classification accuracy of 0.9948, sensitivity of 0.9815, and specificity of 0.9882 for 10 iterations of holdout cross-validation. The performance of the proposed method is also comparable with several earlier reported state-of-the-art models in the literature.

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Authors and Affiliations

  1. Department of Computer and Network Engineering, Jazan University, Jazan, 45142, Saudi Arabia

    Ibrahim AlShourbaji

  2. Department of Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, Maharashtra, 400706, India

    Pramod Kachare

  3. Department of Computer Science, Jazan University, Jazan, 45142, Saudi Arabia

    Waleed Zogaan

  4. Department of Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria

    L. J. Muhammad

  5. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

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  1. Ibrahim AlShourbaji
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  2. Pramod Kachare
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  3. Waleed Zogaan
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  4. L. J. Muhammad
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  5. Laith Abualigah
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Correspondence to L. J. Muhammad.

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

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AlShourbaji, I., Kachare, P., Zogaan, W. et al. Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis. SN COMPUT. SCI. 3, 229 (2022). https://doi.org/10.1007/s42979-022-01129-6

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  • DOI: https://doi.org/10.1007/s42979-022-01129-6

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