Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images

Abstract

Breast cancer is the most common type of cancer in women and the leading cause of death from a malignant growth in the world. Machine learning methods have been created to help with cancer detection accuracy. There are several methods for detecting cancer. Histopathological images are more accurate. In this study, we employed the Gabor filter to extract statistical features from invasive ductal carcinoma histopathology images. From the histopathological images, we chose 100, 200, 400, 1000, and 2000 at random. These statistical features were used to train several models to classify these images as malignant or benign, including the decision tree, quadratic discriminant analysis, extra randomized trees, gradient boosting, Gaussian process, Naive Bayes, nearest centroid, multilayer perceptron, and support vector machine. The models' accuracy, sensitivity, specificity, precision, and F1_score were examined. The models produced the highest results when there were 100 images and a wavenumber of 0.2. While as the number of images increased, the models' effectiveness reduced. The most obvious finding to emerge from this study is that we suggest using deep learning instead of machine learning models for large datasets.

Enhancing CNNs Performance on Object Recognition Tasks with Gabor Initialization

“…They have been used in tasks such as invariant object recognition [1], building and road structure detection from satellite images [1], license plate detection [2], traffic sign recognition [3], diagnosis of invasive ductal carcinoma of the breast [4], edge detection [5], texture segmentation [5], image classification [5], fingerprint and face recognition [5], texture recognition [6], and hyperspectral image classification [7]. Gabor filters are known for their ability to extract essential activations, their multi-orientation and multi-scale analysis capabilities, and their effectiveness in texture classification and feature extraction [3,4,7]. They are suitable for texture recognition in computer vision due to their optimal properties in the spatial and frequency domains [3].…”