P. Rodrigues, Getúlio Igrejas, Romeu Ferreira Beato
{"title":"Obtaining Deep Learning Models for Automatic Classification of Leukocytes","authors":"P. Rodrigues, Getúlio Igrejas, Romeu Ferreira Beato","doi":"10.4018/978-1-7998-3095-5.ch001","DOIUrl":"https://doi.org/10.4018/978-1-7998-3095-5.ch001","url":null,"abstract":"In this work, the authors classify leukocyte images using the neural network architectures that won the annual ILSVRC competition. The classification of leukocytes is made using pretrained networks and the same networks trained from scratch in order to select the ones that achieve the best performance for the intended task. The categories used are eosinophils, lymphocytes, monocytes, and neutrophils. The analysis of the results takes into account the amount of training required, the regularization techniques used, the training time, and the accuracy in image classification. The best classification results, on the order of 98%, suggest that it is possible, considering a competent preprocessing, to train a network like the DenseNet with 169 or 201 layers, in about 100 epochs, to classify leukocytes in microscopy images.","PeriodicalId":207322,"journal":{"name":"Machine Learning and Deep Learning in Real-Time Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127656165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep Learning With TensorFlow","authors":"Shahina Anwarul, Deepa Joshi","doi":"10.4018/978-1-7998-3095-5.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-3095-5.ch004","url":null,"abstract":"This chapter aims to acquaint the users with key parts of TensorFlow and some basic ideas about deep learning. In particular, users will figure out how to perform fundamental calculations in TensorFlow and implementation of deep learning using TensorFlow. This chapter intends to gives a straightforward manual for the complexities of Google's TensorFlow framework that is easy to understand. The basic steps for the installation and setup of TensorFlow will also be discussed. Starting with a simple “Hello World” example, a practical implementation of deep learning problem to identify the handwritten digits will be discussed using MNIST dataset. It is only possible to understand deep learning through substantial practical examples. For that reason, the authors have included practical implementation of deep learning problems that motivates the readers to plunge deeply into these examples and to get their hands grimy trying different things with their own ideas using TensorFlow because it is never adequate to perceive algorithms only theoretically.","PeriodicalId":207322,"journal":{"name":"Machine Learning and Deep Learning in Real-Time Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126069521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}