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Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems, Vol. 25, MIT Press, 1097-1105
https://kr.nvidia.com/content/tesla/pdf/machine-learning/imagenet-classification-with-deep-convolutional-nn.pdf

has been cited by the following article:

• TITLE: Mammogram Classification with HanmanNets Using Hanman Transform Classifier

  AUTHORS: Jyoti Dabass, Madasu Hanmandlu, Rekha Vig, Shantaram Vasikarla

  KEYWORDS: Mammograms, ResNet 18, Hanman Transform Classifier, Abnormality, Diagnosis, VGG-16, AlexNet, GoogleNet, HanmanNets

  JOURNAL NAME: Journal of Modern Physics, Vol.15 No.7, June 28, 2024

  ABSTRACT: Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.