چکیده مقاله
Deep learning based algorithms have shown agreat success on different tasks including image classification One of the requirements of implementing deep learningapproaches is availability of large scale datasets However, thelack of big medical datasets due to the difficulties in recordingthese kinds of data, is one of the major problems inimplementing deep learning approaches Therefore, dataaugmentation has become an important step for increasing thenumber of data samples Image rotating in different angles,horizontal and vertical flipping is one of the popular imagedata augmentation methods However, the generated imagesare so similar to the original ones Recently, GenerativeAdversarial Neural Networks GANs have been proposed aspowerful methods for generating new data samples In thisarticle, we explore image augmentation by GAN structures tobe used in leukemia diagnosis task To this end, a deepconvolutional GAN is considered for generating white bloodcell images to increase the number of image samples ofALLIDB database Then, a deep Convolutional NeuralNetwork is applied on the augmented dataset to classify theimages as normal or leukemia Experimental results verify thatby implementing GAN approach for image augmentation wecan achieve to 84%, classification accuracy which is 10%improvement with respect to the common augmentationmethod
کلیدواژهها
نویسندگان
Zohreh Ansari
Assistant ProfessorBiomedical Engineering Dept Meybod UniversityMeybod, Yazd, Iran
شیوه ارجاع
Ansari, Zohreh,1401,Data Augmentation by Generative AdversarialNetworks for White Blood Cell ImageClassification,1st International Conference and 6th National Conference on Computers, information technology and applications of artificial intelligence
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی و ششمین کنفرانس ملی کامپیوتر، فناوری اطلاعات و کاربردهای هوش مصنوعی3 اسفند 1401 · اهواز