An improved convolutional neural network for abnormality detection and segmentation from human sperm images

Abstract

Recent days, the infertility is affecting one of every ten couples. This makes the negative effect on the quality of a couple’s life, social causes, and psychological problems. Here the sperm morphology analysis helps to diagnose this problem. Here the machine learning approach is used for classification, detection and segmentation process. This also utilizes morphology approach for image representation. In this proposed method, the deep convolutional neural network is used for detecting the abnormality of human male infertility. Here the image morphological process is employed with the enhanced Otsu’s threshold method for segmenting the sperm image, which helps to detect the abnormal region using convolution layer. Here the database is collected from the human sperm image analysis dataset. Initially, the morphological process is applied to reduce the noise from the given set of input image then the segmentation process is performed by using E-Otsu’s threshold method. Two-dimensional Otsu’s thresholding technique reduces the computation complexity and it uses the median filter and for edge reduction approach sobel operator is used, which improves the performance of segmentation. Overall, the proposed research work optimizes three sections that are image representation by morphology approach, image segmentation by Enhanced-Otsu’s thresholding approach, and abnormality detection by Convolutional Neural Network. This method obtains the result of accuracy, detection rate, and computation time. By comparing with the existing method, the proposed method achieves the 98.99% of accuracy result and detects the abnormality effectively with the reduced computation time of 4 min and 15 s. This proposed work is done by using MATLAB with the adaptation of 2018a.

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Correspondence to L. Prabaharan.

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Prabaharan, L., Raghunathan, A. An improved convolutional neural network for abnormality detection and segmentation from human sperm images. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-020-02773-7

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Keywords

  • Sperm images
  • Deep Convolutional Neural Network
  • Enhanced Otsu’s threshold method
  • Median filtering
  • Segmentation
  • Abnormality detection