Abstract

The demand for techniques based on computer vision are constantly increasing due to the development of techniques for decision making pertaining to medical, social and other primary disciples of day to day life. Image processing is a subset of computer vision in which the computer vision systems make use of the image processing algorithms to carry out vision emulation for recognizing objects. This study deal with the construction of convolution neural networks (CNNs) based on deep learning. It is used for classifying chest X-ray images into two classes (Normal, Abnormal) and executed on a graphics processing unit (GPU) based high performance computing platform. Medical image classification is one of the important tasks in many medical imaging applications. Tuberculosis is a communicable disease for which early diagnosis critical for disease control. Manual screening for tuberculosis identification involves a labour-intensive task with poor sensitivity and specificity. To improve diagnosis in medical images there is in need of better classification techniques. This paper proposes CNN to classify lung X-ray images with better classification accuracy and low error rate. The data available for medical image classification problems are insufficient to train accurate and robust classifier. The data augmentation technique helps to generate more new samples from the available images using label-preserving transformations. In this paper various augmentation techniques are implemented such as horizontal flips, vertical flip, rotation (fewer angle), crops, scale right and left, are used for capturing important characteristics of medical images, and they are applied to classification function. Later little work has been done to determine which augmented strategy is best for medical image classification. Here various augmentation results are compared and evaluated to show the better augmentation techniques. It is concluded that shear lead to validation accuracies of 93% and horizontal and vertical flips gives the least accuracy of 53% of accuracy.

Keyword

Data augmentation, Flips, Rotates filters, Convolutional neural network, Shift, Scale, Shear, Tuberculosis.

Cite this article

J.RamaC.NaliniA.Kumaravel

Refference

[1][1]Ciompi F, Chung K, Van Riel SJ, Setio AA, Gerke PK, Jacobs C, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Scientific reports. 2017; 7:46479.

[2][2]Hussain Z, Gimenez F, Yi D, Rubin D. Differential data augmentation techniques for medical imaging classification tasks. In AMIA annual symposium proceedings 2017 (p. 979). American Medical Informatics Association.

[3][3]Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: overview, challenges and the future. In Classification in BioApps 2018 (pp. 323-50). Springer, Cham.

[4][4]Inoue H. Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929. 2018.

[5][5]Fawzi A, Samulowitz H, Turaga D, Frossard P. Adaptive data augmentation for image classification. In international conference on image processing 2016 (pp. 3688-92). IEEE.

[6][6]Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H. Synthetic data augmentation using GAN for improved liver lesion classification. In international symposium on biomedical imaging 2018 (pp. 289-93). IEEE.

[7][7]Wong SC, Gatt A, Stamatescu V, McDonnell MD. Understanding data augmentation for classification: when to warp? In international conference on digital image computing: techniques and applications 2016 (pp. 1-6). IEEE.

[8][8]Salehinejad H, Valaee S, Dowdell T, Colak E, Barfett J. Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks. In international conference on acoustics, speech and signal processing 2018 (pp. 990-4). IEEE.

[9][9]Kumar A, Wang YY, Liu KC, Tsai IC, Huang CC, Hung N. Distinguishing normal and pulmonary edema chest x-ray using Gabor filter and SVM. In international symposium on bioelectronics and bioinformatics 2014 (pp. 1-4). IEEE.

[10][10]Zhang J, Xia Y, Wu Q, Xie Y. Classification of medical images and illustrations in the biomedical literature using synergic deep learning. arXiv preprint arXiv:1706.09092. 2017.

[11][11]Kori A, Krishnamurthi G, Srinivasan B. Enhanced Image Classification with data augmentation using position coordinates. arXiv preprint arXiv:1802.02183. 2018.

[12][12]Gupta A, Chopra S, Ledig C. Generative bone lesions synthesis for data augmentation in X-ray.2018.

[13][13]Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621. 2017.

[14][14]https://cdn.ymaws.com/siim.org/resource/resmgr/siim2018/abstracts/18posters-Borstelmann.pdf. Accessed 12 November 2018.

[15][15]Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu KI, et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists detection of pulmonary nodules. American Journal of Roentgenology. 2000; 174(1):71-4.

[16][16]Jaeger S, Candemir S, Antani S, Wang YX, Lu PX, Thoma G. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quantitative Imaging in Medicine and Surgery. 2014; 4(6):475-77.