Diagnosis Of Breast Cancer Disease Research Articles

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Breast cancer is the most common type of cancer in the world, affecting both men and women. In 2023, the American Cancer Society's reported that there will be approximately 297,800 new cases of invasive breast cancer in women and 2,850 in men, along with 55,750 cases of ductal carcinoma in situ (DCIS) in women. Further, an estimated 43,750 deaths are expected from breast cancer, of which approximately 43,180 are among women and 570 are among men. In this paper, we propose an automated grading system for breast cancer based on tumor's histopathological images using a combination of the Histogram of Oriented Gradients (HOG) for feature extraction and machine learning algorithms. The proposed system has four main phases: image preprocessing and segmentation, feature extraction, classification, and integration with a website. Grayscale conversion, enhancement, noise and artifact removal methods are used during the image preprocessing stage. Then the image is segment during the segmentation phase to extract regions of interest. And then, features are extracted from the obtained region of interest using the Histogram of Oriented Gradients (HOG) algorithm. The next, the images are classified into three distinct breast cancer grades based on the extracted features using machine learning algorithms. Moreover, the effectiveness of the proposed system was evaluated and reported using vary evaluation methods and the results showed a remarkable accuracy of up to 97% by the SVM classifier. Finally, the machine learning model is integrated into a website to improve the detection and diagnosis of breast cancer disease and facilitate the access and use of patient data. This will make the work easier for physicians to enhance breast cancer detection and treatment

Convolutional Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening for Vietnamese patients

ABSTRACT Nowadays, breast cancer is one of the leading cancers in Vietnam, and it causes approximately 6000 deaths every year. The rate of breast cancer patients was calculated as 26.4/100000 persons in 2018. There are 21,555 new cases reported in 2020. However, these figures can be reduced with early detection and diagnosis of breast cancer disease in women through mammographic imaging. In many hospitals in Vietnam, there is a lack of experienced breast cancer radiologists. Therefore, it is helpful to develop an intelligent system to improve radiologists’ performance in breast cancer screening for Vietnamese patients. Our research aims to develop a convolutional neural network-based system for classifying breast cancer X-Ray images into three classes of BI-RADS categories as BI-RADS 1 (“normal”), BI-RADS 23 (“benign”) and BI-RADS 045 (“incomplete and malignance”). This classification system is developed based on the convolutional neural network with ResNet 50. The system is trained and tested on a breast cancer image dataset of Vietnamese patients containing 7912 images provided by Hanoi Medical University Hospital radiologists. The system accuracy uses the testing set achieved a macAUC (a macro average of the three AUCs) of 0.754. To validate our model, we performed a reader study with the breast cancer radiologists of the Hanoi Medical University Hospital, reading about 500 random images of the test set. We confirmed the efficacy of our model, which achieved performance comparable to a committee of two radiologists when presented with the same data. Additionally, the system takes only 6 seconds to interpret a breast cancer X-Ray image instead of 450 seconds interpreted by a Vietnamese radiologist. Therefore, our system can be considered as a “second radiologist,” which can improve radiologists’ performance in breast cancer screening for Vietnamese patients.

Role of Vitamin D Receptor Gene Polymorphism with Steroid Receptors in Breast Cancer: an Update

This review touches on new insights into the possible role of Vitamin D receptors genetic polymorphisms, and steroid receptors in the patients diagnosed with breast cancer. This is the most common cancer in women; further, it creates diverse illnesses among the diseased patients, and the prognosis is linked with the different subtypes present in the hormone receptors. Most of the review studies focus on the epidemiology of the disease. However, fewer studies are done on the genes polymorphism prognosis, which plays a more significant role in the prognosis and diagnosis of breast cancer, so by proper screening at the genetic level as early, can predict the disease in females which will help the clinicians in better management of the disease. Hence the burden of disease and its ill effects can be reduced in the patient care system. Thus the Vitamin D levels in the serum and the vitamin D receptors (VDR) transcriptionally controls its target genes in the cell proliferation, differentiation, and death pathways in a ligand-dependent manner, providing protection against cancer growth and progression. Also, there are strong relationships between VDR polymorphisms and steroid hormone (estrogen, progesterone, and androgen) receptors, which will help in the prognosis and diagnosis of breast cancer disease. We recommend early screening of these receptors using advanced molecular biology techniques like Real-time Polymerase chain reaction, Chemiluminescence, Western blotting which will help to detect the genotyping of these genes at the earlier stages and are non-invasive, patient-friendly, reliable, and accurate. Vitamin D receptor gene polymorphism and steroid receptors themselves can act as early predictive biomarkers for many studies that are to be warranted further in the different ethnic populations with large sample sizes.

Medical Image Prediction for Diagnosis of Breast Cancer Disease Comparing the Machine Learning Algorithms: SVM, KNN, Logistic Regression, Random Forest, and Decision Tree to Measure Accuracy

Machine learning using data mining techniques are used rapidly in medical research to predict the disease diagnosis. The aim of this study is to evaluate the performance of SVM, KNN, Logistic Regression, Random Forest, and Decision Tree. Materials and Methods: A Total of 569 samples are collected from the UCI Machine Learning Laboratory. The samples are divided into benign and malignant cells using groups like SVM, KNN, Decision Tree, Random Forest, and Logistic Regression to compare the performance of benign and malignant cells. The required samples for this analysis are done by G power calculation. Minimum power of analysis is fixed as 0.8 and maximum accepted error is fixed as 0.5. Results: Logistic Regression prediction appears to be better accuracy of 95% than SVM, KNN, Decision Tree, and Random Forest of 92%, 90%, 85%, 91%. Significance of this proposed system is likely to be 0.55. Conclusion: In this study, it is found that Logistic Regression appears to be better than the SVM, KNN, Decision Tree, and Random Forest in breast cancer detection using the Wisconsin dataset.

DNA‐Gated N‐CDs@SiO2 Nanoparticles‐Based Biosensor for MUC1 Detection

AbstractThe ultrasensitive detection of mucin1 (MUC1), a typical breast cancer marker, is of great significance for the diagnosis of breast cancer. Here, a biosensor based on DNA‐gated nitrogen‐doped carbon quantum dots‐loaded hollow mesoporous silica nanoparticles (DNA‐gated N‐CDs@SiO2 NPs) for highly sensitive detection of MUC1 were reported. The preparation process and properties of DNA‐gated N‐CDs@SiO2 NPs were characterized in detail. Results of experiments of detection of MUC1 and cell imaging indicate that the biosensor with MUC1‐specific DNA sequence is beneficial for selective detection of MUC1 by MUC1‐triggered unlocking of DNA gate and controlled release of N‐CDs. The biosensor exhibits satisfactory results with a low detection limit of 10 nM and good specificity, as well as MUC1‐positive cell imaging performance, demonstrating the potential for MUC1 detection, which provides a new method for early screening and diagnosis of breast cancer diseases.

A novel machine learning approach for breast cancer diagnosis

Breast cancer disease is a major public health problem among women worldwide. This article proposes an expert system for the diagnosis of breast cancer disease based on an evolutionary algorithm known as Differential Evolution (DE) of a Radial-Based Function Kernel Extreme Learning Machines (RBF-KELM). In the structure of the RBF-KELM, there are two adjustable parameters of the RBF-kernel which are the penalty parameter C and the RBF-kernel's parameter (σ). These parameters play a major role in the efficiency of RBF-KELM. In this study, the optimal values of these parameters have been obtained using a differential evolution (DE) algorithm. To validate the effectiveness of the suggested approach, DE-RBF-KELM was examined on the two datasets: The Mammographic Image Analysis Society (MIAS) and the wisconsin breast cancer database (WBCD) and the results were satisfactory compared to conventional approaches.

A Novel Score Fusion Approach for Breast Cancer Diagnosis

Meme kanseri tüm dünyada yaygın bir hastalık olması sebebiyle hastalığın erken teşhisi, hastaların bu hastalıktan tamamen kurtulabilmeleri açısından kritik öneme sahiptir. Hastalığın teşhisini kolaylaştırmak için tıp doktorları bilgisayar destekli uzman sistemlerden yararlanabilmektedir. Bu çalışmada meme kanseri veri örneklerini iyi huylu veya kötü huylu sınıflarına ayırmak için genel regresyon sinir ağı (Generalized Regression Neural Network-GRNN) ve ileri beslemeli sinir ağı (Feed Forward Neural Network-FFNN) temelli bir skor füzyon yöntemi önerilmiştir. Önerilen yöntem Wisconsin Teşhis Meme Kanseri (Wisconsin Diagnostic Breast Cancer-WDBC) veri seti üzerinde test edilmiştir. Bu iki temel ağın ve önerilen yöntemin kullanışlılığı incelenmiş ve performans sonuçları karşılaştırmalı olarak sunulmuştur. Önerilen yöntem sınıflandırma doğruluğu bakımından literatürde WDBC veri setini kullanarak yapılan mevcut çalışmalar ile kıyaslanmıştır. Elde edilen deneysel sonuçlar önerilen yöntemin, meme kanseri teşhisi için umut vadettiğini ve tıp uzmanlarının hastalığa ilişkin karar vermelerinde yardımcı bir araç olarak kullanılabileceğini göstermektedir.

Swarm Optimized Modular Neural Network Based Diagnostic System for Breast Cancer Diagnosis

Artificial Neural Networks have long been considered a simple yet powerful and elegant paradigm for solving problems related to Pattern Recognition, Machine Learning and Knowledge Discovery. However, performance of traditional, monolithic neural network based systems, suffers when faced with complex problems which involve a large number of decision variables or dimensions.Also, performance of any such system depends on the architecture of the neural network involved. The architecture usually remains sub-optimal as human expertise is generally used to design the optimal architecture. In this paper, we describe how the twin paradigms of modularity and swarm intelligence based optimization could be successfully used to overcome these concerns. Here, instead of using a single monolithic expert, we use a modular neural network where several independent neural network experts ind ividually work upon the inputs and give their outputs which is then integrated using an Integrator (here, a Fuzzy C -Means Integrator). Also, swarm intelligence has been used to determine the connections in each individual expert for achieving an optimizedarchitecture for each expert. This approach has been used for the diagnosis of breast cancer disease. Experimental results showthat the proposed approach gives a better diagnostic ability than those of other traditional methods used.