Multi-Method Diagnosis of Histopathological Images for Early Detection of Breast Cancer Based on Hybrid and Deep Learning

Breast cancer (BC) is a type of suffered by adult females worldwide. A late diagnosis BC leads to death, so early essential for saving lives. There are many methods diagnosing BC, including surgical open biopsy (SOB), which however constitutes an intense workload pathologists follow SOB and additionally takes long time. Therefore, artificial intelligence systems can help accurately earlier; it tool that assist doctors in making sound diagnostic decisions. In this study, two proposed approaches were applied, each with systems, diagnose dataset magnification factors (MF): 40×, 100×, 200×, 400×. The first method hybrid technology between CNN (AlexNet GoogLeNet) models extracts features classify them using the support vector machine (SVM). Thus, all datasets diagnosed AlexNet + SVM GoogLeNet SVM. second diagnoses ANN based on combining handcrafted extracted fuzzy color histogram (FCH), local binary pattern (LBP), gray level co-occurrence matrix (GLCM), collectively called fusion features. Finally, fed into neural network (ANN) classification. This has proven its superior ability histopathological images (HI) accurately. algorithm achieved results 100% metrics 400× dataset.