A Deep Convolutional Neural Network Architecture for Cancer Diagnosis using Histopathological Images
Karthika Gidijala1, Mansa Devi Pappu2, Manasa Vavilapalli3, Mahesh Kothuru4
1Karthika Gidijala*, Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, India.
2Mansa Devi Pappu, Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, India. 
3Manasa Vavilapalli, Department of Computer Science and Engineering, Dadi Institute of Engineering and Technology, Visakhapatnam, India.
4Mahesh Kothuru, Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed University, Visakhapatnam, India.
Manuscript received on September 29, 2021. | Revised Manuscript received on October 05, 2021. | Manuscript published on October 30, 2021. | PP: 7-12 | Volume-10 Issue-12, October 2021. | Retrieval Number: 100.1/ijitee.L952410101221 | DOI: 10.35940/ijitee.L9524.10101221
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Abstract: Many different models of Convolution Neural Networks exist in the Deep Learning studies. The application and prudence of the algorithms is known only when they are implemented with strong datasets. The histopathological images of breast cancer are considered as to have much number of haphazard structures and textures. Dealing with such images is a challenging issue in deep learning. Working on wet labs and in coherence to the results many research have blogged with novel annotations in the research. In this paper, we are presenting a model that can work efficiently on the raw images with different resolutions and alleviating with the problems of the presence of the structures and textures. The proposed model achieves considerably good results useful for decision making in cancer diagnosis.
Keywords: Histopathological images of breast cancer, Deep Learning, CNN.