Open AccessEffectively Diagnosing Malaria by Optimizing the Hyperparameters of CNN using Genetic Algorithm on the Multi core GPU
Author(s) -
Manjit Jaiswal,
Aditya Kumar Sahu,
Mudasar Zafar
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8448.038620
Subject(s) - hyperparameter , computer science , convolutional neural network , artificial intelligence , multi core processor , task (project management) , genetic algorithm , pattern recognition (psychology) , segmentation , image (mathematics) , machine learning , parallel computing , management , economics
Image classification is an important task in computer vision involving a large area of applications such as object detection, localization and image segmentation. When it comes to image classification, the most adopted methods are based on deep neural network and especially convolutional Neural Networks(CNN). Selection of hyperparameters plays a crucial role in performance of model and it comes by experience. So, in this paper, we will use the genetic algorithm(GA) to automate and build the CNN model for higher accuracy on GPU which is provided by Google Collaboratory cloud. The best architecture of CNN after several generations of the genetic algorithm is then compared to the state-of-the-art CNN. We have used the malaria cell images dataset to find out whether the person is normal or if they are suffering from malaria. We trained two types of malaria cells, which are uninfected and parasitized on Tesla P100 multi core GPU. We got a high training accuracy of 97% and got a testing accuracy of about 95% on the multicore GPU that boosted the speed of execution of training time period and testing time period.