Hybrid deep CNN-LSTM network for breast histopathological image classification

Abstract

Author(s): Ankita Patra, Santi Kumari Behera and Nalini Kanta Barpanda

Breast cancer is a serious public health concern since it is associated with the highest rates of cancer-related morbidity globally. The likelihood of effective treatment and deaths are increased with early detection. Sadly, it is a challenging and urgent task requiring pathologists' skills. Automatic breast cancer identification based on histopathological pictures dramatically benefits patients and their future. In order to categorize breast histopathology images, this research intends to offer a deep learning technique that combines a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM). In this system, deep feature extraction is performed by CNN, while detection utilizing the extracted feature is performed by LSTM. For experimentation, BACH 2018 dataset is used, which includes 800 images of four kinds, i.e., normal, benign, invasive, and in situ. The achieved accuracy, sensitivity, specificity, precision, and computational time are 98.1%, 95.5%, 96.4%, 97.2%, and 24 seconds respectively

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Awards Nomination

Editors List

  • Prof. Elhadi Miskeen

    Obstetrics and Gynaecology Faculty of Medicine, University of Bisha, Saudi Arabia

  • Ahmed Hussien Alshewered

    University of Basrah College of Medicine, Iraq

  • Sudhakar Tummala

    Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh

     

     

     

  • Alphonse Laya

    Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis

     

  • Fava Maria Giovanna

     

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