… neural networks though the number of parameters of MoC-CNN [10] (170M) is much smaller
… multiple CNNs. We propose a semantic segmentation method by integrating multiple CNNs …

… We address the problem of segmenting cell contours … cells (iRPE) using Convolutional
Neural Networks (CNN). Our goal is to compare the accuracy gains of CNN-based segmentation …

… database, the trained CNN to segment abnormal cells, and all of … CNN to other types of
medical images, in this work we investigate the use of CNNs for cervical cell image segmentation…

… cell image segmentation method using Generative Adversarial Network (GAN) with multiple
… Semantic segmentation using CNN is also applied to cartography [20, 21], automatic driving …

… Ideally, the deeper the CNN’s architecture design, the better the results. Therefore, … CNNs
by [15], [19], [20], we propose an improved U-net algorithm for medical cell image segmentation…

… The “Asan” public dataset was used for the classification with CNN and they achieved
accuracy of 0.97 for the basal cell carcinoma and melanoma, accuracy of 0.84 for “squamous cell …

… in medical image segmentation works [39]–[41], we propose a hyper-dense architecture for
multi-modal image segmentation that extends the concept of dense connectivity to the multi-…

… The main aim of our proposed work is to segment and classify blood cells using K means
algorithm, and also with the help of image processing techniques. A complete blood cell count …

… in the CNN nuclei … multi-layer random walker image segmentation for nuclei-seeded region
growing. In this same phase, we also find cell cytoplasm candidates by thresholding the CNN …

… Significantly, research conducted in the 2010s applied image processing, feature extraction,
and conventional machine learning algorithms. For instance, Saeedizadeh et al. [5] (2015) …