An Automatic Breast Cancer Diagnostic System Based on Mammographic Images Using Convolutional Neural Network Classifier

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ID: 313250
2022
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Abstract
World’s second most occurring cancer is breast cancer. Prediction of disease is one of the most challenging tasks and there are many factors that effect this type of diagnosis like the ability of visual perception. This paper proposed a Convolutional Neural Network (CNN) based proper method for analyzing the earliest signs of breast cancer with the help of mammogram images. The main goal of proposed system is to identify the disease of breast cancer at early stages. Due to this reason, Mammographic image analysis society (MIAS) dataset is used. There are three hundred & twenty two (322) mammograms in the dataset, with 209 images of normal breasts and 133 images of abnormal breasts. While abnormal breasts are further classified as benign (62 images) and malignant (51 images). To implement this system, python library Keras and Tensor Flow libraries are used along with deep learning model CNN. Convolutional Neural Network (CNN) has been shown to be effective in detecting breast cancer in mammography images with 70% accuracy rate, according to promising testing data. The proposed system will enable the radiologist in detecting breast cancer in early stages.
Reference Key
imported_1777060033_69ebc8c11ceba Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Imran Ihsan
Journal Journal of Computing & Biomedical Informatics
Year 2022
DOI
10.56979/401/2022/93
URL
https://jcbi.org/index.php/Main/article/view/93
Keywords Keywords not found

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