Mammogram Classification with HanmanNets Using Hanman Transform Classifier  

作　　者：Jyoti Dabass Madasu Hanmandlu Rekha Vig Shantaram Vasikarla Jyoti Dabass;Madasu Hanmandlu;Rekha Vig;Shantaram Vasikarla(EECE Department, The Northcap University, Sector 23-A, Gurugram, India;CSE Department, MVSR Engineering College, Nadergul, Hyderabad, India;CSE Department, The Northcap University, Sector 23-A, Gurugram, India;CS Department, California State University, Northridge, CA, USA)

机构地区：[1]EECE Department, The Northcap University, Sector 23-A, Gurugram, India [2]CSE Department, MVSR Engineering College, Nadergul, Hyderabad, India [3]CSE Department, The Northcap University, Sector 23-A, Gurugram, India [4]CS Department, California State University, Northridge, CA, USA

出　　处：《Journal of Modern Physics》2024年第7期1045-1067,共23页现代物理（英文）

摘　　要：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.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.

关 键 词：MAMMOGRAMS ResNet 18 Hanman Transform Classifier ABNORMALITY DIAGNOSIS VGG-16 AlexNet GoogleNet HanmanNets 

分 类 号：TH7[机械工程—仪器科学与技术]