Amazing technological breakthrough possible @S-Logix pro@slogix.in

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • pro@slogix.in
  • +91- 81240 01111

Social List

Detection and Diagnosis of Breast Cancer Using Deep Learning - 2021

Detection And Diagnosis Of Breast Cancer Using Deep Learning

Research Area:  Machine Learning

Abstract:

Breast Cancer (BC) is a cancerous growth that is a result of uncontrolled cell division in the mammary tissues, usually in the ducts and in the lobules. BC is the most dominant fast-growing cancer and one of the leading cause of cancer mortality in women. BC incidents are increasing swiftly every year around the world especially in developing countries due to grown life expectancy and assumption of western culture. The conventional process of detecting BC involves a clinical expert who observed the medical images of affected breast tissues and looks for structural changes, irregularities in cell forms, ordination of cells in the tissue and determining the stage of the cancer. As conventional interpretation is often time consuming, expensive and error prone; computer-aided detection (CAD) technique is used as an alternative to provide a more accurate, automatic, fast and reproducible procedure to detect BC. This research presents a fully automatic process of BC detection. Two well know filter such as Gaussian Blur (GB) and Detail Enhanced (DE) filter has been used here for the preprocessing purpose. Convolutional Neural Network (CNN) classifier has been used here for classification. The proposed model is performed on an openly accessible dataset named Breast Histopathology Image dataset and the outcome exhibits the sharpness of our proposed model. The obtained accuracy is 87.49%, 88.46% and 88.10% in Case-I, Case-II and Case-III, respectively.

Keywords:  

Author(s) Name:   Mohammad Ashik Alahe; Md. Maniruzzaman

Journal name:  

Conferrence name:  IEEE Region 10 Symposium (TENSYMP)

Publisher name:  IEEE

DOI:  10.1109/TENSYMP52854.2021.9550975

Volume Information: