A novel deep learning method for automatic assessment of human sperm images. (June 2019) - Javadi, Soroush; Mirroshandel, Seyed Abolghasem
Abstract: Sperm morphology analysis (SMA) is a very important factor in the diagnosis process of male infertility. This research proposes a novel deep learning algorithm for malformation detection of sperm morphology using human sperm cell images. Our proposed method detects and analyzes different parts of human sperms. First of all, we have prepared an image collection, called the MHSMA dataset, which can be used as a standard benchmark for future machine learning studies in this problem. This collection consists of 1, 540 sperm images from 235 patients with male factor infertility. This unique dataset is freely available to the public. After applying data augmentation techniques, we have proposed a sampling method for fixing data imbalance. Then, we have designed a deep neural network architecture and trained it to detect morphological deformities in different parts of human sperm—head, acrosome, and vacuole. Our proposed method is one of the first algorithms that considers the acrosome. In addition, our method can work very well with non-stained and low-resolution images. Our experimental results on the proposed benchmark show the high accuracy of our deep learning algorithm for detection of morphological deformities from images. In these experiments, the proposed algorithm has achieved F 0.5 scores of 84.74 %, 83.86 %, and 94.65 % in acrosome, head, and vacuole abnormality detection, respectively. It should be noted that our algorithm achieves a better accuracy than existing state-of-the-art methods in acrosome and vacuole abnormality detection on the proposed benchmark. Also, our method works very fast. It can classify images in real-time, even on a mainstream laptop computer. This allows an embryologist to quickly decide whether or not the analyzed sperm should be selected. Graphical abstract: Image 1 Highlights: Automatic assessment of human sperms' morphological deformities in head, acrosome, neck, tail, and vacuole. Designing an effective and low computational cost deep learning model. Ability to work on low resolution images and unstained sperms. High accuracy & Real-time processing time. Preparing a freely available dataset with 1, 540 images.