Edge detection based on Hodgkin-Huxley neuron model simulation.

In this paper, we propose a spiking neural network model for edge detection in images. The proposed model is biologically inspired by the mechanisms employed by natural vision systems, more specifically by the biologically fulfilled function of simple cells of the human primary visual cortex that are selective for orientation. Several aspects are studied in this model according to three characteristics: feedforward spiking neural structure; conductance-based model of the Hodgkin-Huxley neuron and Gabor receptive fields structure. A visualized map is generated using the firing rate of neurons representing the orientation map of the visual cortex area. We have simulated the proposed model on different images. Successful computer simulation results are obtained. For comparison, we have chosen five methods for edge detection. We finally evaluate and compare the performances of our model toward contour detection using a public dataset of natural images with associated contour ground truths. Experimental results show the ability and high performance of the proposed network model.

Multi-features extraction based on deep learning for skin lesion classification.

For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural networks (CNN) architectures as feature extractors and of 24 machine learning classifiers to evaluate the classification of skin lesions from two different datasets: ISIC 2019 and PH2. In this research, we find out that a DenseNet201 combined with Fine KNN or Cubic SVM achieved the best results in accuracy (92.34% and 91.71%) for the ISIC 2019 dataset. The results also show that the suggested method outperforms others approaches with an accuracy of 99% on the PH2 dataset.