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An Optimized Deep Learning Techniques for Analyzing Mammograms


Satish Babu Bandaru, Natarajasivan. D, Rama Mohan Babu. G


Vol. 23  No. 7  pp. 39-48


Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application’s starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt 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 taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm’s in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.


Mammography, Convolutional Neural Networks (CNNs), Visual Geometry Group Network (VGG Net), Residual Network (Res Net), Teaching Learning Based Optimization Algorithm (TLBO)