Detection Of Breast Abnormalities Research Articles

Automated system utilizing non-invasive technique mammograms for breast cancer detection

In order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient's therapy and recovery is crucial. This study's objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic). To ascertain if the tissue is malignant or benign, the examination's findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95.32%, while the RF satisfied a far higher 98.83 percent.

Innovative infrared imaging approach for breast cancer screening: Integrating rotational thermography and machine learning analysis

This paper presents a novel approach to breast cancer screening using infrared (IR) imaging. This work encompasses four phases: Refining data collection, advancing analysis methods, and enhancing feature extraction with machine learning. The developed system employed a temperature-controlled chamber with rotational thermography techniques to maintain consistent temperatures and capture high-quality IR images and all possible subject views. The paper describes four key experiments to detect breast cancer using IR imaging. The experiments involved the use of dynamic temperature-based data collection and a semi-circular arc movement to ensure precise imaging, keeping the object in focus. Initial experiments involved the use of dynamic temperature-based data collection and a semi-circular arc movement to ensure precise imaging focus. The final experiment incorporated a semi-circular arc movement. For each subject, 32 thermal IR images were acquired, targeting one breast at a time while isolating the other with an IR-proof barrier. The collected datasets were used for breast abnormality detection. The analyzed results revealed that support vector machine and neural network algorithms achieved an accuracy rate of 93.18%. The system’s installation at a hospital in India allowed for real-world application and validation. The final study, which introduced a new IR imaging protocol, demonstrated improved results compared to earlier pilot studies. This method enhances the accuracy of distinguishing malignant and benign tumors, supporting early breast cancer detection and treatment. The proposed methodology addresses data collection and analysis challenges, leading to improved screening efficiency and better patient outcomes.

Comparison between BIRAD Scores and Histological features in Diagnosis of Breast Neoplasia at Warith International Cancer Institution.

Introduction: Accurate evaluation and classification of breast lesions using the Breast Imaging-Reporting and Data System (BIRADS) are crucial for appropriate management and treatment planning of breast cancer. This study explores the relationship between BIRADS scores and histological features in the diagnosis of breast neoplasia at the Warith International Cancer Institution in Karbala, Iraq. Methods: This descriptive study included 520 patients diagnosed with breast neoplasia with a pre-diagnostic malignancy probability (BI-RADS 3, 4, or 5) between June 2023 and June 2024. Based on the diagnostic imaging method, patients were classified into mammography and ultrasonography groups. Clinicopathological factors, including age, BI-RADS categories, and histological findings, were analyzed. Results: Mammogram findings revealed that 63.65% of patients had benign results, while 20.77% had findings indicative of malignancy. Ultrasound findings showed that 71.35% of patients had benign results, and 11.35% had findings suggestive of malignancy. Histological analysis indicated that 23.85% of patients had malignant results, 68.07% had benign findings, and 2.31% showed suspicious results. Patients aged 50 years and above had a significantly higher likelihood of detecting malignant breast lesions through mammography compared to younger patients. At the same time, no significant association was found between age groups and ultrasound findings. Conclusion: Mammography demonstrated high sensitivity and specificity in detecting breast neoplasia, although there were false positives and negatives. Ultrasound screening also effectively detected breast abnormalities, particularly in identifying lesions that required further evaluation. The study highlights the importance of age in influencing breast cancer detection through mammography, with older patients having a higher detection rate than younger patients.

Measurement Methodology: Breast Abnormality Detection using Thermography: An Engineer's Perspective

Measurement Methodology: Breast Abnormality Detection using Thermography: An Engineer's Perspective

Early detection of self-breast examination using smartphone breast application.

Breast self-examination (BSE) is a screening method that can help empower, raise awareness, and detect breast abnormalities in women. The use of smartphone applications for breast cancer early detection is able to overcome some of these barriers. This study aimed to conduct a systematic review to assess the effectiveness of the effect of using smartphone application-based technology on breast cancer. Google Scholar, PubMed, Proquest, and ScienceDirect were searched for "breast self-examination" and "smartphone application" literature. This literature's study designs were quasi-experimental, prospective cohort, qualitative, and systematic review. The study contained five unique research projects with 828 varied participants. The use of smartphones was seen to have advantages in terms of increased knowledge, perceived vulnerability, barriers to breast self-examination (BSE), self-efficacy, health motivation, and BSE practices. The utilization of smartphone apps related to BSE is shown through the availability, accessibility, and focus of information and is in line with the Health Belief Model (HBM).

Automatic Classification of Cancerous Masses in Digital Mammograms Using Curvelet Analysis and Hybrid Genetic Algorithm-Artificial Neural Network Model

Background: Mammography is the most fundamental and widely used method for detecting breast abnormalities. Distinguishing malignant from benign lesions requires extracting relevant information, which can be challenging and time-consuming for radiologists. Computer-aided diagnosis (CAD) techniques can serve as complementary diagnostic tools, assisting radiologists in the early detection and analysis of abnormalities in mammograms. Objectives: This study aimed to propose a CAD system for extracting significant features of abnormalities in breast mammograms using Curvelet transform and fractal analysis, and classifying breast tumors as malignant or benign based on the calculated features. Patients and Methods: In this study, an efficient feature extraction method was applied, utilizing Curvelet transform and fractal analysis, on a dataset comprising 113 abnormal images from the Mammographic Image Analysis Society (MIAS) database, which included 62 benign and 51 malignant cases. The method yielded 575 features, but due to potential irrelevance or redundancy, a multi-objective optimization (MOO) approach based on a genetic algorithm (GA) for an artificial neural network (ANN), named GA-MOO-ANN, was proposed to obtain and focus on an optimal and effective feature set. As a result of this approach, a set of 17 efficient features was extracted. The proposed algorithm was implemented in MATLAB 2014a, and the performance metrics were calculated using 6-fold cross-validation. Results: The experimental results demonstrated exceptional performance, with an accuracy (Acc) of 98.2%, specificity (Sp) of 100%, sensitivity (Se) of 96.8%, positive predictive value (PPV) of 100%, negative predictive value (NPV) of 96.2%, and an impressive area under the curve (AUC) of 0.98, providing comparable results to other recent methods. Conclusion: The current findings suggest that the proposed method could be a valuable tool for breast cancer diagnosis, potentially reducing the number of unnecessary breast biopsies. This method may lead to more efficient patient evaluation and earlier detection of breast tumors.

Unsupervised feature correlation model to predict breast abnormal variation maps in longitudinal mammograms

Breast cancer continues to be a significant cause of mortality among women globally. Timely identification and precise diagnosis of breast abnormalities are critical for enhancing patient prognosis. In this study, we focus on improving the early detection and accurate diagnosis of breast abnormalities, which is crucial for improving patient outcomes and reducing the mortality rate of breast cancer. To address the limitations of traditional screening methods, a novel unsupervised feature correlation network was developed to predict maps indicating breast abnormal variations using longitudinal 2D mammograms.The proposed model utilizes the reconstruction process of current year and prior year mammograms to extract tissue from different areas and analyze the differences between them to identify abnormal variations that may indicate the presence of cancer. The model incorporates a feature correlation module, an attention suppression gate, and a breast abnormality detection module, all working together to improve prediction accuracy. The proposed model not only provides breast abnormal variation maps but also distinguishes between normal and cancer mammograms, making it more advanced compared to the state-of-the-art baseline models. The results of the study show that the proposed model outperforms the baseline models in terms of Accuracy, Sensitivity, Specificity, Dice score, and cancer detection rate.

Genetic algorithm based detection of breast cancer using least square-support vector machine classifier

Breast tumors are a dangerous disease among women worldwide. They are the second leading cause of death among all forms of cancers in women. Their early detection is critical to increasing the survival rate of women. Mammography is a reliable screening technique in the early detection of abnormal breast tissue severity. Radiologist abnormalities in the breast tissue, radiologists employ mammography. However, detecting breast abnormalities through digital diagnostic techniques by a radiologist could be time consuming. Consequently, computerized studying of digital mammography has emerged via the development of CAD systems. Several CAD systems have been developed for breast cancer detection. However, obtaining a satisfactory performance of CAD systems is a challenging task. We propose a CAD architecture for the classification of breast tissues as either benign or malignant using an LS-SVM classifier with various kernels namely linear, quadratic, polynomial, MLP, and RBF kernels. From the experimental outputs, it is clear that GA based LS-SVM classifier with RBF kernel outputs classification accuracy of 94.59% for normal/abnormal case classification is better, when it is compared with all other kernels. It is also stated that GA based LS-SVM classifier with RBF kernel produces a better classification accuracy of 98.26% for benign/malignant case classification when it is compared with other reported works.

Breast Mass Detection and Classification Using Machine Learning Approaches on Two-Dimensional Mammogram: A Review.

Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.

Exploring enablers and barriers to breast self-examination among women in the North Shewa Zone, Oromia, Ethiopia: a qualitative study

Breast cancer (BC) is the leading cause of cancer death worldwide and the second most common cancer overall. Breast self-examination (BSE) is one of the cheapest methods used for the early detection of BC in asymptomatic women. More than 90% of cases of BC can be detected by women themselves. This reality stresses the importance of BSE as the key BC detection mechanism. However, in Ethiopia, most of the BE studies were not conducted among women in the general population. Therefore, this study aimed to explore enablers and barriers to breast self-examination among women in the North Shewa Zone, Oromia. A descriptive qualitative study design was conducted from August 1, 2022, to September 30, 2022. Five focus group discussions (FGDs) were conducted with 46 women from one selected district in the North Shewa Zone, Oromia. A Purposive sampling technique was used to select participants for FGD. The audio-recorded data were transcribed verbatim to “Afan Oromo”. Transcribed data were translated into English. The data were manually coded into themes and analyzed manually by using inductive thematic analysis. The findings of the study were discussed under five themes of enablers and three themes of barriers. The five themes of enablers were knowledge about BC, knowledge about BSE, experience of BSE practice, perceived susceptibility, and perceived benefit of BSE practice. The four themes of barriers were low knowledge of BSE practice, misconceptions about BSE practice, and fear of detecting BC. These findings suggest that targeted health education programs, collaboration between healthcare providers and local stakeholders, and the availability of support services can play a crucial role in overcoming barriers and encouraging BSE practice for early detection of breast abnormalities.

Automated Breast Boundary Segmentation to Improve the Accuracy of Identifying Abnormalities in Breast Thermograms

Breast thermography is a non-invasive, painless, affordable, and safer method of detecting breast abnormalities. Thermal imaging techniques used for detecting breast anomalies rely on precise breast boundary segmentation. However, the segmentation accuracy of the breast boundary region is affected by unclear boundaries, a low signal-to-noise ratio, and poor contrast of the thermal images. To mitigate this, this paper proposes a novel Distance-based Metrics and High-Temperature Region-based Adaptive Thresholding (DM-HTRAT) method for breast boundary region segmentation and dissecting left and right breasts precisely. In the first section, the Upper Region Boundary (URB) and the intersection point of the breast are segmented using the Distance-based Metrics (DM) method. In the second section, a High-Temperature Region-based Adaptive Thresholding (HTRAT) method is used to segment the Lower Region Boundary (LRB). The result of the segmentation is analysed both quantitatively and qualitatively, also it is compared with the state-of-the-art methods. The proposed method has a significantly better accuracy of 96.5% than the other methods. This segmented image is then fed into the abnormality detection system developed via the asymmetrical analysis of segmented breast boundary regions. The proposed DM-HTRAT-based breast boundary segmentation is robust in segmenting all types of breast thermal images and aids in the more reliable and effective detection of breast abnormalities.

Penyuluhan dan Demonstrasi Tentang Perawatan Payudara pada Ibu Hamil Sebagai Upaya Persiapan Pemberian ASI Ekslusif

This study explains the importance of exclusive breastfeeding (EBF) for babies because it provides the necessary nutrients and antibodies to protect them from various diseases. Low EBF can increase infant mortality. Although there has been an increase in EBF globally, it is still below the 50% target set by the World Health Organization (WHO) for the period 2015-2020. In Indonesia, the percentage of EBF for infants aged 0-5 months has increased to 71.58% in 2021 from 69.62% in the previous year, but some provinces still have percentages below the national average. Lack of education, socialization, advocacy, and campaigning towards successful breastfeeding is a problem in achieving EBF. The method used in this study is counseling and demonstration methods about improper breast care can lead to insufficient milk supply, and mothers need support and information to prepare for breastfeeding. Proper breast care can help mothers produce enough milk, prevent breast infections, and detect breast abnormalities. Health education about breast care is most effective when given to pregnant women during the third trimester. The study concluded that providing health education about proper breast care during pregnancy is critical to encouraging EBF and improving infant health.

Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis.

This article aims to provide an updated overview of the indications for diagnostic breast magnetic resonance imaging (MRI), discusses the available and novel imaging exams proposed for breast cancer detection, and discusses considerations when performing breast MRI in the clinical setting. Breast MRI is superior in identifying lesions in women with a very high risk of breast cancer or average risk with dense breasts. Moreover, the application of breast MRI has benefits in numerous other clinical cases as well; e.g., the assessment of the extent of disease, evaluation of response to neoadjuvant therapy (NAT), evaluation of lymph nodes and primary occult tumor, evaluation of lesions suspicious of Paget's disease, and suspicious discharge and breast implants. Breast cancer is the most frequently detected tumor among women around the globe and is often diagnosed as a result of abnormal findings on mammography. Although effective multimodal therapies significantly decline mortality rates, breast cancer remains one of the leading causes of cancer death. A proactive approach to identifying suspicious breast lesions at early stages can enhance the efficacy of anti-cancer treatments, improve patient recovery, and significantly improve long-term survival. However, the currently applied mammography to detect breast cancer has its limitations. High false-positive and false-negative rates are observed in women with dense breasts. Since approximately half of the screening population comprises women with dense breasts, mammography is often incorrectly used. The application of breast MRI should significantly impact the correct cases of breast abnormality detection in women. Radiomics provides valuable data obtained from breast MRI, further improving breast cancer diagnosis. Introducing these constantly evolving algorithms in clinical practice will lead to the right breast detection tool, optimized surveillance program, and individualized breast cancer treatment.

Modern Artificial Intelligence Enhanced Breast Thermography: Better Cameras, Better Algorithms have led to Acceptable Clinical Performance

Infrared thermal imaging technology has been used in medicine since the 1960s (1) but fell out of favour with the introduction of more sophisticated imaging technologies. Recently, there has been a global resurgence in the utilisation of thermal imaging in many regions of the body (2) (Figure 1A). This has been possible due to improvements in camera capability along with development of advanced computational techniques to assist in reading of images. This report will review the important innovations that have renewed the utilisation of breast thermography to detect breast abnormalities.

Knowledge, Attitude and Practice of Breast Self-Examination in Nigeria: A 10 Year Systematic Review

Background: Among the screening methods for the early detection of breast abnormalities that could lead to breast cancer, Breast self-examination is the basic, simplest, cost effective, self-administered and very important technique. It allows women to get acquainted with the nature of their breasts and therefore allows them to discover any deviation. Early detection goes a long way in successful treatment of BC and this is easily come by with good and adequate knowledge, good attitude towards and excellent skill to administer BSE. Consequently, we drew out the data on BSE knowledge, attitudes and practice among Nigeria women. Methods: A systematic review was done among studies conducted in Nigeria using Arskey and O’Malleys’ framework as a guide. The databases searched include PubMed, Google, Google Scholar, Jstor, Intech Open, Proquest and PubMed Central for relevant studies on women’s knowledge, attitude and practice on BSE. Studies included in the review were from thirteen states in Nigeria. The articles reviewed in this systematic review were journals published between August 2012 to July 2022, a 10-year systematic review. Results: Sixty-three (63) articles were firstly extracted to be eligible for review; thirty-seven (37) were excluded due to duplication of content, lack of focus on the concept and incomplete content. Another nine (9) were discarded for muddling up result with another concept and for not involving at least two of the variables, i.e., knowledge, attitude and practice of breast self-examination. The seventeen (17) articles were conducted in thirteen states of which two (2) were conducted in Osun State as well as Sokoto State and three in Oyo State while one (1) each in other ten states, namely Benin, Kano, Imo, Akwa-Ibom, Ekiti, Ebonyi, Abia, Ondo, Awka, and Lagos States. Twelve (12) studies reported evidence on BSE knowledge, attitude and practice; three (3) on knowledge and practice; and two (2) reported on attitude and practice of BSE. The findings of this study submit varying knowledge levels on BSE though—averagely good knowledge but poor practice among women in Nigeria. Conclusion/Recommendation: The findings from these studies show that the knowledge of BSE among women folk in Nigeria is averagely low though women’s attitude seems good. Similarly, the practice of BSE among women in Nigeria is poor. Therefore, proactive measures need to be taken to increase the level of knowledge of BSE through educational programs among every category of women to reduce morbidity and mortality from breast cancer in Nigeria in the nearest future.

Persistent Homology for Breast Tumor Classification Using Mammogram Scans

An important tool in the field of topological data analysis is persistent homology (PH), which is used to encode abstract representations of the homology of data at different resolutions in the form of persistence barcode (PB). Normally, one will obtain one PB from a digital image when using a sublevel-set filtration method. In this work, we built more than one PB representation of a single image based on a landmark selection method, known as local binary patterns (LBP), which encode different types of local texture from a digital image. Starting from the top-left corner of any 3-by-3 patch selected from an input image, the LBP process starts by subtracting the central pixel value from its eight neighboring pixel values. Then, each cell is assigned with 1 if the subtraction outcome is positive, and 0 otherwise, to obtain an 8-bit binary representation. This process will identify a set of landmark pixels to represent 0-simplices and use Vietoris–Rips filtration to obtain its corresponding PB. Using LBP, we can construct up to 56 PBs from a single image if we restrict to only using the binary codes that have two circular transitions between 1 and 0. The information within these 56 PBs contain detailed local and global topological and geometrical information, which can be used to design effective machine learning models. We used four different PB vectorizations, namely, persistence landscapes, persistence images, Betti curves (barcode binning), and PB statistics. We tested the effectiveness of the proposed landmark-based PH on two publicly available breast abnormality detection datasets using mammogram scans. The sensitivity and specificity of the landmark-based PH obtained was over 90% and 85%, respectively, in both datasets for the detection of abnormal breast scans. Finally, the experimental results provide new insights on using different PB vectorizations with sublevel set filtrations and landmark-based Vietoris–Rips filtration from digital mammogram scans.

Tingkat Pengetahuan Wanita Usia Subur (WUS) Tidak Berhubungan Dengan Sikap, dan Perilaku Pemeriksaan Payudara Sendiri (SADARI)

Breast cancer is one of the non-communicable diseases that is ranked second in the world that mostly affects women. The cause of high cases of death from breast cancer can occur due to delays in detecting breast abnormalities. One way to detect breast cancer is to perform a breast self-examination (BSE). The correct way to do breast self-examination must be supported by experience and attitude in practicing BSE. This study aims to determine how the level of knowledge, attitudes, and behavior of women of childbearing age relate to breast self-examination. This research is a descriptive-analytic study with a cross-sectional approach. This research was conducted at the Faculty of Medicine, the Universitas Muslim Indonesia using a total sampling technique of 114 people according to the inclusion and exclusion criteria. The results of this study indicate that the majority of respondents are at the age of 18 years (57%) and age 20 years (3%). The level of knowledge to realize as much (69.3%) with good knowledge and as much as (30.7%) with less knowledge. Knowledge of the attitudes and behavior of breast self-examination that is good (53.5%) and less (46.5%), based on the results of the chi-square test shows that there is no significant relationship between the level of knowledge with attitudes and behavior how to do consciously with p-value = 0.482. The conclusion is that there is no relationship between the level of knowledge with the attitudes and behavior of women of childbearing age on breast self-examination (BSE).

Hybrid Detection of Breast Abnormalities Based on Contrast Agents: Introducing a Proof of Concept from a Physics Perspective.

This manuscript attempts to present a proof of concept from a physics perspective of a hybrid detective system based on the utilization of contrast agents with the purpose of indicating breast tissue abnormalities. In the present concept, the photon-counting module of the detector is set up to the K-characteristic radiation emitted by the contrast agent. Τwo X-ray spectra were used: 40 kV- W/Al (1.6 mm) and 50 kV- W/Al (1.6 mm) with additional filtration of 0.3 mm Gd. Iodine (I) contrast agent was studied as a ‘‘fingerprint’’ for tissue abnormality indication. A computational Monte Carlo model, based on previously published validated tabulated data and tissue experimental measurements, was developed with the purpose of showing that the present concept has practical potential; however, with a weakness of not being accompanied by experimental validation. The study considered two types of internal tissue layers (fibrous/tumor with thickness values of 0.2–2.5 mm) within an external layer of fat tissue (4 and 8 cm). Quantitative (number of encountered K-photons) and qualitative (tumor–fibrous ratio) advantages of using X-ray spectra of a higher tube voltage (50 kV) and of counting the Κα photons were found. In addition, the quantitative and qualitative benefits were correspondingly more dominant at high (2.5 mm) and low (0.2 mm) tissue thickness values. In conclusion, by utilizing suitable contrast agents as ‘‘fingerprint’’ tissue abnormalities, the acquisition of combined morphological and functional imaging features (through the counting of K-X-rays) could enhance breast imaging in its present form and lead to advanced prognostic capabilities of breast abnormalities.

MMSHRs: a morphology model of suspicious hyperthermic regions for degree of severity prediction from breast thermograms

ABSTRACT The presence of suspicious hyperthermic regions (SHRs) in breast thermograms is a prominent indicator of breast pathology, for which delineation and analysis of SHRs have a crucial role in early detection of breast abnormalities. A novel approach for breast abnormality grading, namely the morphology model of suspicious hyperthermic regions (MMSHRs), is proposed here. The proposed model first segments SHRs from breast-thermograms and then analyzes their morphology to grade the thermograms according to their degree of severity. To segment SHRs, a simple but effective method that computes the similarity score of each pixel with the highest intensity value is designed. . The performance of the proposed segmentation method is tested on both public and in-house-captured datasets. With the optimal values of seven evaluation metrics, the proposed segmentation method outperforms other state-of-the-art segmentation methods. The values of evaluation metrics further justify that the proposed SHRs segmentation method addresses all the limitations regarding infrared breast thermogram segmentation, and reduces the under-segmentation and over-segmentation of SHRs. Following segmentation of SHRs, the MMSHRs extract the corresponding morphological features, allowing the classification of thermograms into mild and severely abnormal with the classification accuracy of 91% and area under the receiver operating characteristic curve of .9998.

Breast Self-Examination Knowledge and its Determinants among Female Students at Addis Ababa University, Ethiopia: An Institution-Based Cross-Sectional Study.

Introduction Breast self-examination is a noninvasive, low-cost screening method for breast cancer detection. A thorough awareness of breast self-examination enables the early detection of breast abnormalities and dramatically lowers breast cancer complications and mortality. The purpose of this study was to investigate the level of knowledge about breast self-examination and its associated factors among female students at Addis Ababa University, Ethiopia. Methods An institution-based cross-sectional study design was employed. The final calculated sample size was 407, and participants were recruited using a proportionate stratified random sampling approach. For data entry and coding, EPI Data 3.1 statistical software was utilized, and for data analysis, SPSS version 18 was employed. The data was described using descriptive analysis. Bivariate and multivariate logistic regression analyses were performed to determine the strength of the association between the predictor and the outcome variables. A 95% confidence interval and a p-value of less than 0.05 were used to declare statistical significance. Results The findings of this study revealed that 49.9% of respondents possessed good breast self-examination knowledge. Previously, urban residents were about two times more likely to have good knowledge of BSE than their rural counterparts (AOR =2.16, 95% CI (1.18–39.91), p =0.011). The odds of having good BSE knowledge were about three times more likely among those who had a good attitude than those who had a poor attitude (AOR =3.17, 95% CI (2.02–4.74), p <0.001). Those who knew someone with a diagnosis of breast cancer were almost three times more likely to have good knowledge than those who did not know (AOR =2.95, 95% CI (1.77-4.91), p <0.001). Conclusion According to the findings of this survey, less than half of the students who participated had good knowledge of breast self-examination. This justifies raising awareness about breast self-examination among female students.