Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features

Abstract

Breast cancer is a disease that affects women in different countries around the world. The real cause of breast cancer is particularly challenging to determine, and early detection of the disease is necessary for reducing the death rate, due to the high risks associated with breast cancer. Treatment in the early period can increase the life expectancy and quality of life for women. CAD (Computer Aided Diagnostic) systems can perform the diagnosis of the benign and malignant lesions of breast cancer using technologies and tools based on image processing, helping specialist doctors to obtain a more precise point of view with fewer processes when making their diagnosis by giving a second opinion. This study presents a novel CAD system for automated breast cancer diagnosis. The proposed method consists of different stages. In the preprocessing stage, an image is segmented, and a mask of a lesion is obtained; during the next stage, the extraction of the deep learning features is performed by a CNN—specifically, DenseNet 201. Additionally, handcrafted features (Histogram of Oriented Gradients (HOG)-based, ULBP-based, perimeter area, area, eccentricity, and circularity) are obtained from an image. The designed hybrid system uses CNN architecture for extracting deep learning features, along with traditional methods which perform several handcraft features, following the medical properties of the disease with the purpose of later fusion via proposed statistical criteria. During the fusion stage, where deep learning and handcrafted features are analyzed, the genetic algorithms as well as mutual information selection algorithm, followed by several classifiers (XGBoost, AdaBoost, Multilayer perceptron (MLP)) based on stochastic measures, are applied to choose the most sensible information group among the features. In the experimental validation of two modalities of the CAD design, which performed two types of medical studies—mammography (MG) and ultrasound (US)—the databases mini-DDSM (Digital Database for Screening Mammography) and BUSI (Breast Ultrasound Images Dataset) were used. Novel CAD systems were evaluated and compared with recent state-of-the-art systems, demonstrating better performance in commonly used criteria, obtaining ACC of 97.6%, PRE of 98%, Recall of 98%, F1-Score of 98%, and IBA of 95% for the abovementioned datasets.

Hybrid Feature Selection Using the Firefly Algorithm for Automatic Detection of Benign/Malignant Breast Cancer in Ultrasound Images

“…Based on this feature vector, the proposed scheme is used to examine BC detection and the results are depicted in Table 5. [21] 98.61 Irfan et al [22] 98.97 Jabeen et al [23] 99.10 Sahu et al [58] 98.13 Cruz-Ramos et al [59] 97.60 Raza et al [60] 99.35 Proposed work 99.50 9(c) shows the confusion matrix, and Figure 9(d) shows the RoC curve. This table also demonstrates the effectiveness of the proposed scheme in detecting BC from UI with improved overall performance when compared with other binary classifiers.…”

Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features

“…HOG is a shape descriptor which is widely used for shape feature analysis (Ranjbarzadeh et al 2022 ; Cruz-Ramos et al 2023 ). The shape pattern characteristics are calculated based on the gradient and edge pixel’s orientation.…”

From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection

“…The Mini-DDSM dataset is not as extensively utilized in published studies as the CBIS-DDSM. [24][25][26] This discrepancy can be attributed to the fact that CBIS-DDSM encompasses a more diverse range of information for each mammogram image as the Mini-DDSM doesn't contain information like the abnormality type, BI-RADS assessment, subtlety score, mass shape and margin, and calcification type and distribution (Supplementary table 4).…”