Cancer Cell Profiling Using Image Moments and Neural Networks with Model Agnostic Explainability: A Case Study of Breast Cancer Histopathological (BreakHis) Database

Dmitry Kaplun, Alexander Krasichkov, Petr Chetyrbok, Nikolay Oleinikov, Anupam Garg and Husanbir Singh Pannu
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Dmitry Kaplun: Department of Automation and Control Processes, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia
Alexander Krasichkov: Radio Engineering Systems Department, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia
Petr Chetyrbok: Department of Mathematics and Informatics, V. I. Vernadsky Crimean Federal University, 298635 Yalta, Russia
Nikolay Oleinikov: Department of Mathematics and Informatics, V. I. Vernadsky Crimean Federal University, 298635 Yalta, Russia
Anupam Garg: Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India
Husanbir Singh Pannu: Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India

Mathematics, 2021, vol. 9, issue 20, 1-20

Abstract: With the evolution of modern digital pathology, examining cancer cell tissues has paved the way to quantify subtle symptoms, for example, by means of image staining procedures using Eosin and Hematoxylin. Cancer tissues in the case of breast and lung cancer are quite challenging to examine by manual expert analysis of patients suffering from cancer. Merely relying on the observable characteristics by histopathologists for cell profiling may under-constrain the scale and diagnostic quality due to tedious repetition with constant concentration. Thus, automatic analysis of cancer cells has been proposed with algorithmic and soft-computing techniques to leverage speed and reliability. The paper’s novelty lies in the utility of Zernike image moments to extract complex features from cancer cell images and using simple neural networks for classification, followed by explainability on the test results using the Local Interpretable Model-Agnostic Explanations (LIME) technique and Explainable Artificial Intelligence (XAI). The general workflow of the proposed high throughput strategy involves acquiring the BreakHis public dataset, which consists of microscopic images, followed by the application of image processing and machine learning techniques. The recommended technique has been mathematically substantiated and compared with the state-of-the-art to justify the empirical basis in the pursuit of our algorithmic discovery. The proposed system is able to classify malignant and benign cancer cell images of 40× resolution with 100% recognition rate. XAI interprets and reasons the test results obtained from the machine learning model, making it reliable and transparent for analysis and parameter tuning.

Keywords: microscopic images; image processing; machine learning; neural networks; Zernike moments; PCA; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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