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Classification Of Mammogram Breast Cancer Using Customized Deep Learning Model
  • Anbumani A,
  • * PJayanthi
Anbumani A
Paavai Institutions

Corresponding Author:[email protected]

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* PJayanthi
Kongu Engineering College
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According to GLOBOCAN 2020, breast cancer is the most prevalent cancer and affects many women globally after lung cancer. Detecting and diagnosing breast cancer earlier may decrease the disease’s death rate. A radiologist can use computer-aided detection/diagnosis technologies to help make an early diagnosis of breast cancer. One of the most popular and efficient techniques for identifying and diagnosing breast cancer is mammography. Deep learning architectures called convolutional neural network (CNN) models were developed to classify breast cancer correctly. This paper presents breast cancer classification using a customized deep-learning model. Two custom CNN models are suggested to classify the breast cancer mammography image efficiently. The effectiveness of the suggested classification approach was assessed using three real-time datasets: MIAS, CBIS-DDSM, and INbreast. The outcomes demonstrate that the suggested approach efficiently classifies the image and achieves 98.78%, 96.92%, and 97.84% accuracy for MIAS, CBIS-DDSM, and INbreast datasets.