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Deep Learning Based Image Segmentation with AlexNet Feature Extraction for Classification of Mammogram Images
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| Author:
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T.SATHYA PRIYA, DR.T.RAMAPRABHA
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| Abstract:
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Breast cancer is a crucial health issue among women all over the globe. The early identification of breast cancer using a mammogram leads to a high survival rate. This paper develops a new deep segmentation based AlexNet with Multilayer Perceptron (MLP) model called DS-ANMLP for automated detection and breast cancer classification. At the earlier stage, preprocessing takes place to eradicate the unwanted noise present in the image and also enhance its quality. Then, the preprocessed image undergoes segmentation using Faster Region-based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model. Afterward, the AlexNet model is applied as a feature extractor to take the segmented image's feature vectors. Finally, MLP is utilized for classifying the images into different kinds of breast cancer and standard images. A series of simulations are carried out to ensure the betterment of the DS-ANMLP model. The experimental outcomes demonstrated the superiority of the DS-ANMLP model.
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| Keyword:
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Breast cancer, Classification, Segmentation, Feature extraction, Mammogram
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2021.13.01.690
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| Download:
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