Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India

Research Article

EARLY STAGE DETECTION AND CLASSIFICATION OF BREAST CANCER

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  • @INPROCEEDINGS{10.4108/eai.16-5-2020.2304093,
        author={C Sai Deep Reddy and Yeturi Ram Mohan and S  Chandana and S  Kavya},
        title={EARLY STAGE DETECTION AND CLASSIFICATION OF BREAST CANCER},
        proceedings={Proceedings of the First  International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India},
        publisher={EAI},
        proceedings_a={ICASISET},
        year={2021},
        month={1},
        keywords={mias resized gray-scaled gaussian filter segmented benign ma-lignant neural network predicted class mammogram},
        doi={10.4108/eai.16-5-2020.2304093}
    }
    
  • C Sai Deep Reddy
    Yeturi Ram Mohan
    S Chandana
    S Kavya
    Year: 2021
    EARLY STAGE DETECTION AND CLASSIFICATION OF BREAST CANCER
    ICASISET
    EAI
    DOI: 10.4108/eai.16-5-2020.2304093
C Sai Deep Reddy1,*, Yeturi Ram Mohan1, S Chandana2, S Kavya1
  • 1: Department of CSE, DayanandaSagarAcademy of Technology and Management
  • 2: Department of CSE, Dayananda Sagar Academy of Technology and Management
*Contact email: yeturirammohan@gmail.com

Abstract

One of the major diseases that affect young to old aged women in re-cent times is breast cancer. It almost ranks as the first cause for death in women across the world. The survival rate of people suffering with it ranges some-where between 40% and 60% depending on the development terms of particular countries. Hence, it becomes quite important to be able to diagnose such a dis-ease at a stage as early as possible, so the patient could look out on the available options for treatment. Therefore, in this project, we propose such a breast can-cer detection system which predicts the nature of the cancer, either benign or malignant by processing the mammographic image of the patient. The model basically uses a range of digital image processing techniques and also algo-rithms of ML in the process to output the prediction. It is trained using the MIAS breast cancer dataset. The input image is first resized, gray-scaled, and a gaussian filter is applied on it to remove background noises. It is then segment-ed and fed to the neural network, which gives the output prediction as an integer value (each value corresponding to a predicted class). The project also has a second stage where the severity of the cancer is also detected by taking input of other detailed attributes of the mammogram.