Abstract
The exponential rise in cancer diseases, primarily the breast cancer has alarmed academia-industry to achieve more efficient and reliable breast cancer tissue identification and classification. Unlike classical machine learning approaches which merely focus on enhancing classification efficiency, in this paper the emphasis was made on extracting multiple deep features towards breast cancer diagnosis. To achieve it, in this paper A Deep Hybrid Featured Machine Learning Model for Breast Cancer Tissue Classification named, AlexResNet+ was developed. We used two well known and most efficient deep learning models, AlexNet and shorted ResNet50 deep learning concepts for deep feature extraction. To retain high dimensional deep features while retaining optimal computational efficiency, we applied AlexNet with five convolutional layers, and three fully connected layers, while ResNet50 was applied with modified layered architectures. Retrieving the distinct deep features from AlexNet and ResNet deep learning models, we obtained the amalgamated feature set which were applied as input for support vector machine with radial basis function (SVM-RBF) for two-class classification. To assess efficacy of the different feature set, performances were obtained for AlexNet, shorted ResNet50 and hybrid features distinctly. The simulation results over DDMS mammogram breast cancer tissue images revealed that the proposed hybrid deep features (AlexResNet+) based model exhibits the highest classification accuracy of 95.87%, precision 0.9760, sensitivity 1.0, specificity 0.9621, F-Measure 0.9878 and AUC of 0.960.
Highlights
In the last few years, cancer has emerged as a deadly and major threat to the humanity across the world
We consider the following key facts to design a novel and more reliable solution; (i) most of the existing medical images and machine learning based breast cancer detection system primarily focus on classifier-centric efforts, (ii) merely a few researches considers feature aspect to perform accurate cancer tissue identification and classification, (iii) most of those approaches employing pre-processing, region of interest (ROI) segmentation, feature extraction and classification undergo high computational overhead, which might be even more complex for large scale data, (iv) most of the classical deep learning models apply shallow features to perform classification, and (v) depth performance analyses of the different researches employing same classifier or same data has exhibited varied performance, indicating biasedness of results published
In this paper a novel and first of its kind hybrid deep feature-based machine learning model is developed for breast cancer tissue identification and classification
Summary
In the last few years, cancer has emerged as a deadly and major threat to the humanity across the world. Major algorithms have been developed as classifier-sensitive solution despite the features of a data play vital role towards optimal cancer diagnosis In this reference, deep learning seems to be more viable and potential. Deep learning methods like AlexNet [21] and ResNet [20] have performed better than the major existing machine as well as deep learning approaches Their efficiency turns out to be more polished due to independency towards additional feature extractor [23]. Literatures reveal that extracting deep features and classifying it using certain efficient machine learning model can yield better performance, especially with low data size, which is practical in real-world applications [24]. Considering above stated key inferences, in this paper a highly robust deep hybrid featured machine learning model for breast cancer tissue classification (AlexResNet+) is developed. References used in this manuscript are given at the last
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