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Fuzzy integrated salp swarm algorithm-based RideNN for prostate cancer detection using histopathology images

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Abstract

One of the dreadful diseases in the medical industry is prostate cancer and it is growing at a higher rate among men. Hence, it is a necessity to detect cancer in an early stage due to the alarming increase in the reports. Various techniques are introduced for effective prostate cancer detection using histopathology images. Accordingly, an automatic method is proposed for segmenting and classifying prostate cancer. This paper presents the prostate cancer detection method using histopathology images by proposing the fuzzy-based salp swarm algorithm-based rider neural network (SSA-RideNN) classifier. At first, the input image is fed to the pre-processing step and then the segmentation is performed using Color Space transformation and thresholding. Once the segmentation is performed, the feature extraction is done by extracting multiple kernel scale invariant feature transform features along with the texture features that are extracted based on local optimal oriented pattern descriptor to improve the classification accuracy. Finally, the prostate cancer detection is done based on the proposed fuzzy-based SSA-RideNN, which is developed by integrating fuzzy approach with SSA-RideNN. The performance of the proposed fuzzy-based SSA-RideNN is analyzed using sensitivity, specificity, and accuracy. The proposed fuzzy-based SSA-RideNN produces the maximum accuracy of 0.9190, a maximum sensitivity of 0.9084, and maximum specificity of 0.9, indicating its superiority.

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Abbreviations

SSA-RideNN:

Salp swarm algorithm-based rider neural network

CS:

Color Space

MK-SIFT:

Multiple kernel scale invariant feature transform

LOOP:

Local optimal oriented pattern

WSI:

Whole slide imaging

PSA:

Prostate-specific antigen

SVM:

Support vector machine

ANN:

Artificial neural network

mpMRI:

Multiparametric magnetic resonance imaging

MCIL:

Multiple clustered instance learning

CNN:

Convolutional neural network

LBP:

Local binary pattern

LDP:

Local directional pattern

NN:

Neural network

ROA:

Rider optimization algorithm

MSE:

Mean square error

DBN:

Deep belief neural network

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Authors and Affiliations

  1. Sharad Institute of Technology College of Engineering, Kolhapur, India

    Shashidhar B. Gurav

  2. D Y Patil College of Engineering and Technology, Kolhapur, India

    Kshama V. Kulhalli

  3. Gogte Institute of Technology, Belagavi, India

    Veena V. Desai

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  1. Shashidhar B. Gurav
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  2. Kshama V. Kulhalli
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  3. Veena V. Desai
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Correspondence to Shashidhar B. Gurav.

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Gurav, S.B., Kulhalli, K.V. & Desai, V.V. Fuzzy integrated salp swarm algorithm-based RideNN for prostate cancer detection using histopathology images. Evol. Intel. 15, 1329–1342 (2022). https://doi.org/10.1007/s12065-020-00402-y

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