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

Abstract

Breast cancer is the second most common type of cancer among women, and it can threaten women’s lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a biopsy taken from the patient’s abnormal tissue is an effective way to distinguish between malignant and benign breast cancer tumors. There are many challenges facing pathologists and experts in diagnosing breast cancer, including the addition of some medical fluids of various colors, the direction of the sample, the small number of doctors and their differing opinions. Thus, artificial intelligence techniques solve these challenges and help clinicians resolve their diagnostic differences. In this study, three techniques, each with three systems, were developed to diagnose multi and binary classes of breast cancer datasets and distinguish between benign and malignant types with 40× and 400× factors. The first technique for diagnosing a breast cancer dataset is using an artificial neural network (ANN) with selected features from VGG-19 and ResNet-18. The second technique for diagnosing breast cancer dataset is by ANN with combined features for VGG-19 and ResNet-18 before and after principal component analysis (PCA). The third technique for analyzing breast cancer dataset is by ANN with hybrid features. The hybrid features are a hybrid between VGG-19 and handcrafted; and a hybrid between ResNet-18 and handcrafted. The handcrafted features are mixed features extracted using Fuzzy color histogram (FCH), local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods. With the multi classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 95.86%, an accuracy of 97.3%, sensitivity of 96.75%, AUC of 99.37%, and specificity of 99.81% with images at magnification factor 400×. Whereas with the binary classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 99.74%, an accuracy of 99.7%, sensitivity of 100%, AUC of 99.85%, and specificity of 100% with images at a magnification factor 400×.

Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features

“…Step 1: preprocessing the images involved removing artifacts and noise using a Gaussian filter, applying a face area cropping method, and performing data normalization [ 43 ]. Step 2: Three deep learning models, namely, VGG16, ResNet101, and MobileNet, were utilized to analyze facial features, eye tracking, and then feature map extraction.…”

“…In this study, a hybrid technique was proposed to detect ASD using images of facial features. The process involved performing the series of steps shown in Figure 2: Step 1: preprocessing the images involved removing artifacts and noise using a Gaussian filter, applying a face area cropping method, and performing data normalization [43]. Step 2: Three deep learning models, namely, VGG16, ResNet101, and MobileNet, were utilized to analyze facial features, eye tracking, and then feature map extraction.…”

“…These models generated feature matrices for the ASD dataset, with sizes of 2940 × 670, 2940 × 715, and 2940 × 610. Step 3: The t-SNE method was applied to reduce the dimen- Step 1: preprocessing the images involved removing artifacts and noise using a Gaussian filter, applying a face area cropping method, and performing data normalization [43].…”