An Optimized Deep Learning Techniques for Analysing Mammograms

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Satish Babu Bandaru, Natarajasivan Deivarajan, Rama Mohan Babu Gatram
DOI : 10.14445/22315381/IJETT-V70I7P240

How to Cite?

Satish Babu Bandaru, Natarajasivan Deivarajan, Rama Mohan Babu Gatram, "An Optimized Deep Learning Techniques for Analysing Mammograms " International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 388-398, 2022. Crossref,

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate regarding this application’s starting age and screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learned from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by considering the problem domain knowledge. Normally, this technique will consume a lot of time and computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGGNet), Residual Network (ResNet), and the inception network for classifying mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm’s to predict breast cancers utilizing mammogram images. The proposed TLBO-ResNet is an optimized ResNet with faster convergence than other evolutionary mammogram classification methods.

Mammography, Deep Learning, Convolutional Neural Networks (CNNs), Visual Geometry Group Network (VGGNet), Residual Network (ResNet), Teaching Learning Based Optimization Algorithm (TLBO).

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