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Full-Text Articles in Computer Engineering
Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r
Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer is the most prevalent and crucial cancer type that should be diagnosed early to reduce mortality. Therefore, mammography is essential for early diagnosis owing to high-resolution imaging and appropriate visualization. However, the major problem of mammography screening is the high false positive recall rate for breast cancer diagnosis. High false positive recall rates psychologically affect patients, leading to anxiety, depression, and stress. Moreover, false positive recalls increase costs and create an unnecessary expert workload. Thus, this study proposes a deep learning based breast cancer diagnosis model to reduce false positive and false negative rates. The proposed model has …
A Comparative Study Of Yolo Models And A Transformer-Based Yolov5 Model For Mass Detection In Mammograms, Damla Coşkun, Dervi̇ş Karaboğa, Alper Baştürk, Bahri̇ye Akay, Özkan Ufuk Nalbantoğlu, Serap Doğan, İshak Paçal, Meryem Altin Karagöz
A Comparative Study Of Yolo Models And A Transformer-Based Yolov5 Model For Mass Detection In Mammograms, Damla Coşkun, Dervi̇ş Karaboğa, Alper Baştürk, Bahri̇ye Akay, Özkan Ufuk Nalbantoğlu, Serap Doğan, İshak Paçal, Meryem Altin Karagöz
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer is a prevalent form of cancer across the globe, and if it is not diagnosed at an early stage it can be life-threatening. In order to aid in its diagnosis, detection, and classification, computer-aided detection (CAD) systems are employed. You Only Look Once (YOLO)-based CAD algorithms have become very popular owing to their highly accurate results for object detection tasks in recent years. Therefore, the most popular YOLO models are implemented to compare the performance in mass detection with various experiments on the INbreast dataset. In addition, a YOLO model with an integrated Swin Transformer in its backbone …
Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche
Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche
Electronic Theses and Dissertations
The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …
Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta
Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposed an accurate and fully automated breast cancer early screening system called the "Breast Cancer-Caps". The capsule network is used in this approach for the cancer detection in breast utilizing the thermal infrared images for the first time. This capsule network is trained with the help of Dynamic as well as Static breast thermal images dataset consisting of left, right, frontal views along with a new multiview thermal images. These multiview breast thermal images are fabricated by concatenating the conventional left, frontal and right view breast thermal images. The other current and popular deep transfer learning models such …
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Turkish Journal of Electrical Engineering and Computer Sciences
The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined …
Radar-Based Microwave Breast Cancer Detection System With A High-Performanceultrawide Band Antipodal Vivaldi Antenna, Hüseyi̇n Özmen, Muhammed Bahaddi̇n Kurt
Radar-Based Microwave Breast Cancer Detection System With A High-Performanceultrawide Band Antipodal Vivaldi Antenna, Hüseyi̇n Özmen, Muhammed Bahaddi̇n Kurt
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a novel ultrawide band (UWB) antipodal Vivaldi antenna with three pairs of slots was designed to be used as a sensor in microwave imaging systems for breast cancer detection. The proposed antenna operates in UWB frequency range of 3.05-12.2 GHz. FR4 was used as a dielectric material and as a substrate for forming the antenna that has a compact size of 36 mm x 36 mm x 1.6 mm. Frequency and time domain performance of the proposed antenna have been investigated and results show that it meets the requirements for UWB radar applications with linear phase response, …
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Turkish Journal of Electrical Engineering and Computer Sciences
The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the …
A Novel Multistage System For The Detection And Removal Of Pectoral Muscles Inmammograms, İdi̇l Işikli Esener, Semi̇h Ergi̇n, Tolga Yüksel
A Novel Multistage System For The Detection And Removal Of Pectoral Muscles Inmammograms, İdi̇l Işikli Esener, Semi̇h Ergi̇n, Tolga Yüksel
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting …
Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo
Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo
Electronic Theses and Dissertations
Breast cancer classification can be divided into two categories. The first category is a benign tumor, and the other is a malignant tumor. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing potential errors that can be made by fatigued or inexperienced physicians. This paper proposes a new shape metric based on the area ratio of a circle to classify mammographic images into benign and malignant class. Support Vector Machine is used as a machine learning tool for training and classification purposes. The improved …
Enhanced Diagnostic Accuracy Of Mammograms On A Mobile Device, Sharanya Padmanabhan
Enhanced Diagnostic Accuracy Of Mammograms On A Mobile Device, Sharanya Padmanabhan
Open Access Theses
With the death of a woman every 13 minutes in the US, and one every minute worldwide, due to breast cancer, the need for early detection cannot be overstated. Mammography is a boon for both early detection and screening of breast tumors. It is an imaging system that uses low dose (9mrem) x-rays for examining the breasts, by the electrons reflected from the tissues (thermoelectric effect). However, there are 20% false positives and 10% false negatives in current practice. Hence, there is a critical need for enhancing the accuracy of these mammograms. Towards this, this thesis was aimed at enhancing …
Extracting Fuzzy Rules For The Diagnosis Of Breast Cancer, Ali̇ Keleş, Aytürk Keleş
Extracting Fuzzy Rules For The Diagnosis Of Breast Cancer, Ali̇ Keleş, Aytürk Keleş
Turkish Journal of Electrical Engineering and Computer Sciences
About one million women are diagnosed with breast cancer every year. Breast cancer makes up one-third of all cancer diagnoses in women. Diagnosing breast cancer early is vital for successful treatment. Among the breast cancer screening methods available today, mammography is the most effective, although the low precision rate of breast biopsy caused by mammogram interpretation results in approximately 70% unnecessary biopsies with benign outcomes. The aim of this study was to extract strong diagnostic fuzzy rules for the inference engine of an expert system to be used for the diagnosis of breast cancer. These rules have been extracted through …
A New Intelligent Classifier For Breast Cancer Diagnosis Based On A Rough Set And Extreme Learning Machine: Rs + Elm, Yilmaz Kaya
A New Intelligent Classifier For Breast Cancer Diagnosis Based On A Rough Set And Extreme Learning Machine: Rs + Elm, Yilmaz Kaya
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing …