Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis

2020 ◽  
pp. 1-25
Author(s):  
V. Ajantha Devi ◽  
Anand Nayyar
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6116
Author(s):  
Muhammad Firoz Mridha ◽  
Md. Abdul Hamid ◽  
Muhammad Mostafa Monowar ◽  
Ashfia Jannat Keya ◽  
Abu Quwsar Ohi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Performance Metrics ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Imaging Modalities ◽  
Research Directions ◽  
Comprehensive Survey ◽  
Processing Techniques

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

Density-Assessment for Breast Cancer Diagnosis Using Deep Learning on Mammographic Image: A Brief Study

2021 ◽  
pp. 313-320
Author(s):  
Shaila Chugh ◽  
Sachin Goyal ◽  
Anjana Pandey ◽  
Sunil Joshi ◽  
Mukesh Azad
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Mammographic Image ◽  
Density Assessment

scholarly journals Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review

2021 ◽  
Vol 46 (1) ◽  
Author(s):  
R Rashmi ◽  
Keerthana Prasad ◽  
Chethana Babu K Udupa
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Medical Image ◽  
Human Life ◽  
Medical Image Processing ◽  
Histopathological Analysis ◽  
Image Processing Techniques ◽  
Computer Aided ◽  
Histopathological Images ◽  
Processing Techniques

AbstractBreast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.

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