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

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.

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

“…The advantage of hybrid XAI models is that they can provide a balance between the interpretability and accuracy of the individual models, making it easier for humans to understand how decisions are made. They can also handle complex and non-linear relationships between inputs and outputs, making them wellsuited for problems with a large amount of data and complex relationships [72].…”

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

“…It explores the use of different segmentation algorithms for superpixel generation and compares the CNN models' predictions to medical annotations, finding alignment with human expert knowledge. In [87], LIME was utilized to explain and justify the results of an automatic breast cancer cell image classification. Another study by [88] investigated the use of Permutation Sample Importance (PSI), LIME, and SHAP as model-agnostic XAI .…”

Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

“…Kaplun et al [14] used CNN to analyze breast cancer images and extract features from complex cancer cells. The results of testing are interpreted by local interpretations and quantifiable artificial intelligence.…”