IoT based optical coherence tomography retinal images classification using OCT Deep Net2

被引：1

作者：

Rajan R. ^{[1
]}

Kumar S.N. ^{[2
]}

机构：

[1] Lincoln University College, Kota Bharu

[2] Department of EEE, Amal Jyothi College of Engineering, Kerala, Kottayam

来源：

Measurement: Sensors | 2023年 / 25卷

关键词：

Deep learning; Machine learning; Neural networks; OCT;

D O I：

10.1016/j.measen.2022.100652

中图分类号：

学科分类号：

摘要：

Machine learning algorithms gains prominence in health care sectors for disease diagnosis, classification and prediction. Deep learning architecture gains prominence in real time applications. This research work proposes a novel deep learning architecture, OCT Deep Net2 for the classification of optical coherence tomography images. Four class disease classification was performed in this research work and the proposed deep learning framework OCT Deep Net2 is an extension of OCT Deep Net1 comprising of 30 layers. The OCT Deep Net2 comprises of 50 layers and is termed as dense architecture, comprises of three recurrent modules. The performance validation reveals the efficiency of the OCT Deep Net2 architecture in terms of the performance metrics. Robust results were produced for batch size of 32 and 100 epochs with an accuracy of 98%. An IoT based system was implemented using Raspberry Pi B+ processor, the OCT Deep Net 2 algorithm was written in Python and executed on Google Colab. © 2022 The Authors

引用

共 28 条

[1]

Shen D., Wu G., Suk H.I., Deep learning in medical image analysis, Annu. Rev. Biomed. Eng., 19, (2017)

[2]

Suzuki K., Overview of deep learning in medical imaging, Radiological physics and technology, 10, 3, pp. 257-273, (2017)

[3]

Lostumbo A., Wanamaker C., Tsai J., Suzuki K., Dachman A.H., Comparison of 2D and 3D views for evaluation of flat lesions in CT colonography, Acad. Radiol., 17, 1, pp. 39-47, (2010)

[4]

Deng L., Yu D., Deep learning: methods and applications, Foundations and Trends® in Signal Processing, 7, pp. 197-387, (2014)

[5]

Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networksKnowl, -Based Syst., 187, (2020)

[6]

Ahn S., Deep learning architectures and applications, Journal of Intelligence and Information Systems, 22, 2, pp. 127-142, (2016)

[7]

Valente I.R., Cortez P.C., Neto E.C., Soares J.M., de Albuquerque V.H., Tavares J.M., Automatic 3D pulmonary nodule detection in CT images: a survey, Comput. Methods Progr. Biomed., 124, 1, pp. 91-107, (2016)

[8]

LeCun Y., Jackel L.D., Et al., Learning algorithms for classification: a comparison on handwritten digit recognition, Neural Network.: the statistical mechanics perspective, 261, (1995)

[9]

Simonyan K., Zisserman A., Very deep convolutional networks for large-scale image recognition, Proceedings of the International Conference on Learning Representations, San Deigo, CA, USA, (2015)

[10]

Szegedy C., Liu W., Jia Y., Et al., Going deeper with convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, (2015)

← 1 2 3 →