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Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning - 12/01/21

Doi : 10.1016/j.irbm.2020.12.004 
S.R. Sannasi Chakravarthy , H. Rajaguru
 Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, India 

Corresponding author.
Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Tuesday 12 January 2021

Graphical abstract

Le texte complet de cet article est disponible en PDF.

Highlights

A classification framework is proposed to solve three-class breast cancer problem.
Deep-learning based robust features are extracted using pretrained ResNet18 model.
Extreme Learning Machine is employed for the detection and classification problem.
The performance is further enhanced using the proposed optimization technique.

Le texte complet de cet article est disponible en PDF.

Abstract

Background and objective

Breast cancer, the most intrusive form of cancer affecting women globally. Next to lung cancer, breast cancer is the one that provides a greater number of cancer deaths among women. In recent times, several intelligent methodologies were come into existence for building an effective detection and classification of such noxious type of cancer. For further improving the rate of early diagnosis and for increasing the life span of victims, optimistic light of research is essential in breast cancer classification. Accordingly, a new customized method of integrating the concept of deep learning with the extreme learning machine (ELM), which is optimized using a simple crow-search algorithm (ICS-ELM). Thus, to enhance the state-of-the-art workings, an improved deep feature-based crow-search optimized extreme learning machine is proposed for addressing the health-care problem. The paper pours a light-of-research on detecting the input mammograms as either normal or abnormal. Subsequently, it focuses on further classifying the type of abnormal severities i.e., benign type or malignant.

Materials and methods

The digital mammograms for this work are taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mammographic Image Analysis Society (MIAS), and INbreast datasets. Herein, the work employs 570 digital mammograms (250 normal, 200 benign and 120 malignant cases) from CBIS-DDSM dataset, 322 digital mammograms (207 normal, 64 benign and 51 malignant cases) from MIAS database and 179 full-field digital mammograms (66 normal, 56 benign and 57 malignant cases) from INbreast dataset for its evaluation. The work utilizes ResNet-18 based deep extracted features with proposed Improved Crow-Search Optimized Extreme Learning Machine (ICS-ELM) algorithm.

Results

The proposed work is finally compared with the existing Support Vector Machines (RBF kernel), ELM, particle swarm optimization (PSO) optimized ELM, and crow-search optimized ELM, where the maximum overall classification accuracy is obtained for the proposed method with 97.193% for DDSM, 98.137% for MIAS and 98.266% for INbreast datasets, respectively.

Conclusion

The obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the automatic detection and classification of breast cancer.

Le texte complet de cet article est disponible en PDF.

Keywords : Mammogram images, Health care, Breast cancer, Deep learning, Crow-search, Elm, Chaotic


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