Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

Amel A. Alhussan,Yasser M. Kadah,Nagwan M. Abdel Samee,Vidan F. Ghoneim

doi:10.14569/ijacsa.2021.0121033

Abstract

The application of artificial intelligence techniques in computer aided detection and diagnosis problems has been among the most promising areas with interest from the scientific community and healthcare industry. Recently, deep learning has become the prime tool for such application with many studies focusing on developing variants that optimize diagnostic performance. Despite the widely accepted success of this class of techniques in this application by the scientific community, it is not prudent to consider it as the only tool available for such purpose. In particular, statistical machine learning offers a variety of techniques that can also be applied at a much lower computational cost. Unfortunately, the results from both strategies cannot be directly compared due to the differences in experimental setups and datasets used in available research studies. Therefore, we focus in this study on this direct comparison using the same dataset and similar data preprocessing as the input to both. We compare statistical machine learning to deep learning in the context of computer-aided detection of breast cancer from mammographic images. The results are compared using diagnostic performance metrics and suggest that simpler statistical machine learning techniques may provide better performance with simpler architectures that allow explanation of results.

Highlights

Breast cancer is the most frequently diagnosed cancer and accounts for a significant portion of the total cancer related deaths among women[1]
We address the direct comparison of statistical machine learning and deep learning techniques in the context of computer-aided detection of breast cancer from mammographic images
The statistical machine learning and deep learning systems were implemented on an academic license of Matlab 2020b (Mathworks, Inc.) with Statistics and Machine Learning and Deep Learning toolboxes

Summary

Introduction

Breast cancer is the most frequently diagnosed cancer and accounts for a significant portion of the total cancer related deaths among women[1]. The early detection of cancer in general, and in breast cancer, is crucial to patient survival. The primary imaging modality for such screening is x-ray mammography where two images in craniocaudal and mediolateral oblique directions are taken and examined carefully by a radiologist for early signs of abnormalities including microcalcifications [2]. The resultant images in their digital form have very high resolution and quantization level Computer-aided diagnosis is pursued as a possible solution to this problem. Even though many such systems were proposed early on as applications of the growing artificial intelligent systems, the digital transition of radiology departments made the utilization of such assisting tools more readily available in many applications including mammography

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Conclusion