During an epidemic crisis, medical image analysis namely microscopic analyses are made to confirm or not the existence of the epidemic pathogen in suspected cases. Pathogen are all infectious agents such as a virus, bacterium, protozoa, prion etc. However, there is often a lack of specialists in the handling of microscopes, hence allowing the need to make the microscopic analysis abroad. This results in a considerable loss of time and in the meantime, the epidemic continues to spread. To save time in the analysis of samples, we propose to make the future microscopes more intelligent so that they will be able to indicate by themselves the existence or not of the pathogen of an epidemic in a sample. To have a smart microscope, we propose a methodology based on efficient Convolution Neural Network (CNN) architecture in order to classify epidemic pathogen with five deep learning phases: (1) Training dataset of provided images (2) CNN Training (3) Testing data preparation (4) CNN generated model on testing data and finally (5) Evaluation of images classified. The resulted classification process can be integrated in a mobile computing solution on future microscopes. CNN can improve the accuracy in pathogens diagnosis that are focused on hand-tuned feature extraction implying some human mistakes. For our study, we consider cholera and malaria epidemics for microscopic images classification with a relevant CNN, respectively Vibrio cholerae images and Plasmodium falciparum images. Image classification is the task of taking an input image and outputting a class or a probability of classes that best describes the image. Interesting results have been obtained from the CNN model generated achieving the classification accuracy of 94%, with 200 Vibrio cholera images and 200 Plasmodium falciparum images for training dataset and 80 images for testing data. Although this document addresses the classification of epidemic pathogen images using a CNN model, the underlying principles apply to the other fields of science and technology, because of its performance and its capability to handle more layers than the previous traditional neural networks.
Plant or crop diseases are important items in the reduction of quality and quantity in agriculture. Therefore, the detection and diagnosis of these diseases are very necessary. The appropria te classification with smalt datasets in Deep Learning is a major scientific challenge. Furthermore, it is difficult and expensive to generate labeled data manually according to certain selection criteria. The approaches using transfer learning aims to resolve this problem by recognizing and applying knowledge and abilities learned in previous tasks to nove! tasks (in new domains). Convolutional neural network Feature extraction Classification Precision agriculture Mildew disease In this paper, we propose an approach using transfer learning with feature extraction to build an identification system of mildew disease in pearl millet. The deep learning facilita tes a practically fast and interesting data analysis in precision agriculture. The expected advantage of the proposai is to provide support to stakeholders (researchers and farmers) through the information and knowledge generated by the reasoning process. The experimental result gives an encouraging performance that is the accuracy of 95.00%, the precision of 90.50%, the recall of 94.50% and the fl score of 91.75%.
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