Abnormality Detection in Breast Images Using Deep Convolution Neural Network
Chinu Mog Choudhari
, Jhunu Debbarma , Sudeshna Das , Ankur Biswas
Breast cancer identification, Convolutional Neural Network, Histopathological images, MOBILENETV2
Breast cancer is a fast-rising disease that is occurring at a distressing degree midst the females. However, prediction of breast cancer at its initial phase may be curable and can reduce the hazard of life threats. The histopathological analysis is effective to localize and detect the malignant tumor because of its capability to analyze at cellular level. However, manual inspection followed by analysis is time taking, labor intensive and mostly have strong subjective biasness in making the accurate decision. Therefore, a CPU assisted conclusion system for estimation of the chest tumor stands out to be current field of learning. in this paper, framework design based on the applications of Convolution Neural Network for accurate prediction and classification of normal and abnormal histopathological images. The framework consists of MOBILENETV2 architecture with two output neurons to represent the normal histopathological images and another representing the abnormal histopathological images. The analysis of the framework has been carried out in publicly available BREAKHIs Benchmark Dataset. From the experimental results it is observed that framework achieved accuracy, specificity, and sensitivity of 84.83%, 81.48%, and 87.92% respectively for classification of the logical images.
"Abnormality Detection in Breast Images Using Deep Convolution Neural Network", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.1116 - 1125, November-2022, Available :https://ijsdr.org/papers/IJSDR2211168.pdf
Volume 7
Issue 11,
November-2022
Pages : 1116 - 1125
Paper Reg. ID: IJSDR_202778
Published Paper Id: IJSDR2211168
Downloads: 000347186
Research Area: Computer Science & Technology
Country: Agartala, Tripura, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
